New Zealand's Job Landscape: Industry Contribution to the Country and Current Job Opportunities in the Technology IndustryΒΆ
IntroductionΒΆ
New Zealand faces a number of economic and employment challenges in the current global economic environment. Uncertainty in global markets combined with domestic policy changes have increased the volatility of New Zealand's employment market. In order to gain a deeper understanding of these issues and their potential impacts, this report provides a detailed analysis of New Zealand's employment market.
The key focus areas are:
- GDP Trends and Industry Contributions: Analyzing the overall changes in New Zealand's GDP and identifying the top three contributing industries.
- Employment Opportunities and GDP Contribution: Investigating whether industries that contribute the most to GDP also provide the most job opportunities.
- Job Market Insights by Sector: Evaluating job opportunities across six main industries based on data from Careers.NZ.
- Auckland's Economic and Employment Performance: Assessing Auckland's role as New Zealand's largest city in terms of GDP and vacancy indices (AVIs) from 2010 to 2022.
- Unemployment and Economic Health: Exploring the correlation between unemployment rates, Auckland's AVIs, and GDP.
- Long-Term Job Creation in Auckland: Analyzing the trends in job creation and destruction in Auckland from 1999 to 2022.
- Salary and Employment Prospects in Technology Industry: Investigating the employment prospects and highest salary ranges in the manufacturing and technology industries, as well as for data analyst positions.
By examining these key factors, we hope to better understand the challenges New Zealand currently faces and the opportunities within the technology industry.
Datasets used:ΒΆ
- NZ Industries' Employment Growth Contributors, 2022-2023οΌAPI: https://rep.infometrics.co.nz/new-zealand/employment/growth-contributors%EF%BC%89
- NZ Industries' GDP Growth Contributors, 2022-2023οΌAPI: https://rep.infometrics.co.nz/new-zealand/economy/growth-contributors%EF%BC%89
- Job profilesοΌWeb scraper: https://www.careers.govt.nz/searchresults?tab=jobs%EF%BC%89
- jobs-online-all.xlsx
- high_level_industry_GDP.xlsx
- pro_industry.csv
- industryGDP.csv
- regionjob.csv
Dataset sources:ΒΆ
- Infometrics(https://www.infometrics.co.nz)
- Careers.govt.nz
- Ministry of Business, Innovation and Employment (MBIE)
- Stas NZ (https://infoshare.stats.govt.nz/)
Research QuestionsΒΆ
Discuss the overall GDP changes in New Zealand from 2010 to 2022. What are the top three industrial contributions?
Does the industry that contributes the most GDP bring the same proportion of job opportunities?
What are the job opportunities in the six main industries based on the occupation listed on Careers.NZ?
As the largest city in New Zealand and a major economic hub, is Auckland the best in terms of all vacancy indices (AVIs) and GDP from 2010 to 2022?
How does the unemployment rate in New Zealand relate to Auckland's AVI and GDP?
Does the job creation in Auckland from 1999 to 2022 remain positive, making Auckland the city with the most economic contribution?
What are the employment prospects and highest salary ranges for the manufacturing and technology industries, as well as for data analyst positions?
Executive SummaryΒΆ
Data Acquisition&WragglingΒΆ
Data Acquisition
- API retrieval of 'GDP contribution data and employment by New Zealand industry'.
- Web-scraped a list of 'job profiles' in New Zealand.
- Downloaded tabular data on the labour market and GDP dimensions from the relevant sources as static data sets.
Data Wraggling
- GDP contribution and employment by industry data: First, histograms were plotted to locate missing values. After cleaning the data, a bar chart was generated to examine the distribution.
- Job profile datasetsοΌ A cross-tabulation was created to count the number of job opportunities in each industry after web-scraping the data.
- Job online data: The names of the multi-index columns were processed, and after removing the missing observations, the time series plots were generated.
- Regional Employment DataοΌ Bar charts were created for each variable to examine outlier values. Quarterly data variables were converted to annual variables, which involves grouping data by year and then summing them, and finally, the 'Type' column was pivoted to group the data into 'job creation' and 'job destruction' categories.
- Labour Force Status: Plot histograms and remove outliers. Finally, kernel density plots were used to check the data distribution.
- Median wage: Plotted boxes to examine the data distribution and then converted the data into annual form.
- GDP by region: Locate a missing value and perform data imputation. Growth rates from recent years were used to predict the missing values.
- GDP by industry: Locate missing values and perform data imputation. Create a heatmap to identify the variable most correlated with the missing value column. Then, build a regression model to predict the missing values
Data IntegrationΒΆ
- Preparation: Before merging different datasets, the structure and naming of each table are standardised to ensure consistency across data sources.
- Merge: A custom function was implemented to merge any number of datasets with a common key efficiently. This allows us to input all datasets simultaneously and merge them quickly.
- Big data processing optimisation: Finally, strategies were implemented to optimise the processing of big data, including using Dask DataFrame and HDF5 formats. This phase also included creating a custom function to quickly rename columns. mat:
AnalysisΒΆ
Using group-by, pivot tables, cross-tabulation, and visualization to answer each research question.
# Import python libraries required for this report
import pandas as pd
import json
from time import sleep
from datetime import datetime
import numpy as np
import requests
from bs4 import BeautifulSoup
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import matplotlib
import seaborn as sns
from pylab import rcParams
%matplotlib inline
matplotlib.style.use('ggplot')
import time
1. Data Acquisition&WragglingΒΆ
In this section, the following data was acquired.
Dynamic datasets:
- API-sourced data: Contributions to GDP and employment by industry (based on the ANZSIC Level 1 industry classification standard) in New Zealand.
- Web-scraped data: Over 400 job profiles in New Zealand from careers.govt.nz, including information on salary, entry requirements and job prospects.
Static datasets:
- Job Online Data: All vacancy indices (AVIs) published by the Ministry of Business, Innovation and Employment (MBIE).
- High-level industry GDP contribution data: GDP contributions by primary, goods-producing and services industries.
- Professional, scientific and technical services industry data.
- Regional employment data.
1.1 Dynamic datasetsΒΆ
1.1.1 NZ Industry Contributions to GDP and EmploymentΒΆ
(1) What is the data
This dataset covers numerous *industries in New Zealand* classified to the *ANZSIC Level 1 industry* standard. The data cover two statistical periods, *2022 and 2023*.
GDP contribution: defined as the annual GDP value generated by each industry, measured in millions.
Employment contribution: defined as the number of *filled jobs* within each industry for that year.
(2) How the data was obtained:
- The data were obtained from https://rep.infometrics.co.nz/new-zealand/economy/growth-contributors.
- The website does not provide an open API. However, we found that the data on the website is dynamically generated using JavaScript. By inspecting the page source, we identified the API endpoints corresponding to the relevant tables. Using these APIs, we successfully retrieved the raw data from the tables.
(3) What to do with this dataset
- Check dataset integrity and clean data.
- Extract relevant GDP and employment contribution columns from the original tables, then merge for further analysis.
#The first dataset: GDP contribution
#URL:https://rep.infometrics.co.nz/new-zealand/economy/growth-contributors
#ANZSIC Level 1 industries' contribution to growth, 2022-2023
# Define URL for GDP data retrieval
url1 = 'https://production.infometrics.co.nz/api/rep/data/?series=GDP&year=2022&year=2023&breakdown=GROWTH&area_id=new-zealand&industry_type_code=L1&industry_type_code=TOTAL'
# Send GET request to retrieve GDP data
response1 = requests.get(url1)
data1 = response1.json()
# Expanding nested data with json_normalize
industryGDP_series = pd.json_normalize(data1, 'series', errors='ignore')
industryGDP_values = pd.json_normalize(data1, record_path=['series', 'values'],
meta=['key', ['industry_types', 'industry_type_code'], ['industry_types', 'name']],
errors='ignore')
industryGDP_values.head()
| change_contribution | change | change_total_val | area_name | year_base | year_focus | value_base | value_focus | area_id | area_type | ... | industry_type_code | industry | industry_short | industry_description | percent_total | parent_industry_code | parents | key | industry_types.industry_type_code | industry_types.name | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2.80 | 2.8 | 10201.2 | New Zealand | 2022 | 2023 | 367582.4 | 377783.6 | new-zealand | NZ | ... | TOTAL | total economy | total | For data not broken down by industry | NaN | NaN | NaN | NaN | NaN | gross domestic product |
| 1 | 0.15 | 3.0 | 549.0 | New Zealand | 2022 | 2023 | 18182.6 | 18731.6 | new-zealand | NZ | ... | L1 | agriculture, forestry and fishing | agriculture, forestry, fishing | Includes: growing crops, raising animals, grow... | 4.96 | TOTAL | [{'industry_type_code': 'TOTAL', 'industry_cod... | NaN | NaN | gross domestic product |
| 2 | -0.06 | -7.1 | -216.2 | New Zealand | 2022 | 2023 | 3052.9 | 2836.7 | new-zealand | NZ | ... | L1 | mining | mining | Includes: extraction of naturally occurring mi... | 0.75 | TOTAL | [{'industry_type_code': 'TOTAL', 'industry_cod... | NaN | NaN | gross domestic product |
| 3 | -0.55 | -6.0 | -1988.5 | New Zealand | 2022 | 2023 | 32875.9 | 30887.4 | new-zealand | NZ | ... | L1 | manufacturing | manufacturing | Includes: physical or chemical transformation ... | 8.18 | TOTAL | [{'industry_type_code': 'TOTAL', 'industry_cod... | NaN | NaN | gross domestic product |
| 4 | 0.07 | 2.7 | 260.9 | New Zealand | 2022 | 2023 | 9739.0 | 9999.9 | new-zealand | NZ | ... | L1 | electricity, gas, water and waste services | electricity, gas, water, waste | Includes: provision of electricity, gas throug... | 2.65 | TOTAL | [{'industry_type_code': 'TOTAL', 'industry_cod... | NaN | NaN | gross domestic product |
5 rows Γ 22 columns
# Select the specified column and create a copy
industryGDP = industryGDP_values[['industry_short', 'value_base', 'value_focus']].copy()
industryGDP.head()
| industry_short | value_base | value_focus | |
|---|---|---|---|
| 0 | total | 367582.4 | 377783.6 |
| 1 | agriculture, forestry, fishing | 18182.6 | 18731.6 |
| 2 | mining | 3052.9 | 2836.7 |
| 3 | manufacturing | 32875.9 | 30887.4 |
| 4 | electricity, gas, water, waste | 9739.0 | 9999.9 |
This dataset contains data from the years 2022 and 2023, with 'value_base' and 'value_focus' representing the data for each year. We will perform checks on these two variables and change the column names to improve the clarity of the table.
# Rename Columns
industryGDP.rename(columns={
'industry_short': 'Industry',
'value_base': 'GDP_2022(M)',
'value_focus': 'GDP_2023(M)',
}, inplace=True)
# Setting the graph size
plt.figure(figsize=(10, 4))
# Use index as the x-axis coordinate
index_labels = range(len(industryGDP))
# Plot histograms of both variables separately
plt.subplot(1, 2, 1)
plt.bar(index_labels, industryGDP['GDP_2022(M)'], color='blue', alpha=0.7)
plt.title('GDP_Contribution_2022')
plt.xlabel('Index')
plt.ylabel('Value')
plt.xticks(index_labels)
plt.subplot(1, 2, 2)
plt.bar(index_labels, industryGDP['GDP_2023(M)'], color='green', alpha=0.7)
plt.title('GDP_Contribution_2023')
plt.xlabel('Index')
plt.ylabel('Value')
plt.xticks(index_labels)
plt.tight_layout()
plt.show()
From the graph it's clear that the data at index 0 is an outlier, corresponding to the 'total' row in the dataset. All the other data appears normal, so we only need to remove the 'total' row.
industryGDP = industryGDP.drop(0)
industryGDP.head()
| Industry | GDP_2022(M) | GDP_2023(M) | |
|---|---|---|---|
| 1 | agriculture, forestry, fishing | 18182.6 | 18731.6 |
| 2 | mining | 3052.9 | 2836.7 |
| 3 | manufacturing | 32875.9 | 30887.4 |
| 4 | electricity, gas, water, waste | 9739.0 | 9999.9 |
| 5 | construction | 23404.8 | 23905.0 |
The next step is to obtain data on the employment contribution of each industry.
#The second dataset: Employment contribution
#Contribution to employment by industry: 2023 vs. 2022
#https://rep.infometrics.co.nz/new-zealand/employment/growth-contributors
url2 = 'https://production.infometrics.co.nz/api/rep/data/?series=EMP_FILLED&year=2022&year=2023&breakdown=GROWTH&area_id=new-zealand&industry_type_code=L1'
response2 = requests.get(url2)
data2 = response2.json()
# Expanding nested data with json_normalize
industryEmployment_series = pd.json_normalize(data2, 'series', errors='ignore')
industryEmployment_values = pd.json_normalize(data2, record_path=['series', 'values'],
meta=['key', ['industry_types', 'industry_type_code'], ['industry_types', 'name']],
errors='ignore')
industryEmployment_values.head()
| percent_total | industry_type_code | industry_code | industry | industry_short | industry_description | change_contribution | change | change_total_val | area_name | year_base | year_focus | value_base | value_focus | area_id | area_type | sort_order | key | industry_types.industry_type_code | industry_types.name | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 5.29 | L1 | A | agriculture, forestry and fishing | agriculture, forestry, fishing | Includes: growing crops, raising animals, grow... | -0.05 | -0.8 | -1248.0 | New Zealand | 2022 | 2023 | 147044.0 | 145796.0 | new-zealand | NZ | 9999 | NaN | NaN | employment |
| 1 | 0.22 | L1 | B | mining | mining | Includes: extraction of naturally occurring mi... | 0.01 | 3.9 | 230.0 | New Zealand | 2022 | 2023 | 5932.0 | 6162.0 | new-zealand | NZ | 9999 | NaN | NaN | employment |
| 2 | 9.15 | L1 | C | manufacturing | manufacturing | Includes: physical or chemical transformation ... | 0.21 | 2.2 | 5451.0 | New Zealand | 2022 | 2023 | 246399.0 | 251850.0 | new-zealand | NZ | 9999 | NaN | NaN | employment |
| 3 | 0.76 | L1 | D | electricity, gas, water and waste services | electricity, gas, water, waste | Includes: provision of electricity, gas throug... | 0.04 | 5.7 | 1135.0 | New Zealand | 2022 | 2023 | 19816.0 | 20951.0 | new-zealand | NZ | 9999 | NaN | NaN | employment |
| 4 | 10.45 | L1 | E | construction | construction | Includes: construction of buildings and other ... | 0.35 | 3.3 | 9314.0 | New Zealand | 2022 | 2023 | 278338.0 | 287652.0 | new-zealand | NZ | 9999 | NaN | NaN | employment |
# Select the specified column and create a copy
industryEmployment = industryEmployment_values[['industry_short', 'value_base', 'value_focus']].copy()
industryEmployment.rename(columns={
'industry_short': 'Industry',
'value_base': 'Employment_2022',
'value_focus': 'Employment_2023',
}, inplace=True)
# Setting the graph size
plt.figure(figsize=(10, 4))
# Use index as the x-axis coordinate
index_labels = range(len(industryEmployment))
# Plot histograms of both variables separately
plt.subplot(1, 2, 1)
plt.bar(index_labels, industryEmployment['Employment_2022'], color='blue', alpha=0.7)
plt.title('GDP_Contribution_2022')
plt.xlabel('Index')
plt.ylabel('Value')
plt.xticks(index_labels)
plt.subplot(1, 2, 2)
plt.bar(index_labels, industryEmployment['Employment_2023'], color='green', alpha=0.7)
plt.title('GDP_Contribution_2023')
plt.xlabel('Index')
plt.ylabel('Value')
plt.xticks(index_labels)
plt.tight_layout()
plt.show()
From the analysis of the above figure it can be seen that the observations *indexed 1 and 3* are at lower values, which could be a cause for concern.
To further understand this situation, it is necessary to identify which industry these observations belong.
print(industryEmployment.iloc[1]['Industry'])
print(industryEmployment.iloc[3]['Industry'])
mining electricity, gas, water, waste
Looking at the data, it can be confirmed that the industry with index 1 is *'Mining', while the industry with index 3 is 'Electricity, gas, water supply and waste management'.
In many countries, these sectors, which are essential for economic stability, do not usually generate significant employment due to the high degree of automation and mechanisation. In this context, the low figures are to be expected and should not be regarded as outliers.*
Therefore, no special treatment is required in our data analysis.
For further analysis, the data on each industry's contribution to GDP and employment are then merged in a table.
# Merge two DataFrames based on Industry columns
merged_industrycontribution = pd.merge(industryEmployment, industryGDP, on='Industry')
merged_industrycontribution.head()
| Industry | Employment_2022 | Employment_2023 | GDP_2022(M) | GDP_2023(M) | |
|---|---|---|---|---|---|
| 0 | agriculture, forestry, fishing | 147044.0 | 145796.0 | 18182.6 | 18731.6 |
| 1 | mining | 5932.0 | 6162.0 | 3052.9 | 2836.7 |
| 2 | manufacturing | 246399.0 | 251850.0 | 32875.9 | 30887.4 |
| 3 | electricity, gas, water, waste | 19816.0 | 20951.0 | 9739.0 | 9999.9 |
| 4 | construction | 278338.0 | 287652.0 | 23404.8 | 23905.0 |
color1 = 'lightblue'
color2 = 'salmon'
fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(16, 8))
# the first figureοΌGDP Contribution by Industry for 2022 and 2023
bar_height = 0.35
opacity = 0.8
index1 = range(len(merged_industrycontribution['Industry']))
bar1_1 = axes[0].barh(index1, merged_industrycontribution['GDP_2022(M)'], bar_height, alpha=opacity, color=color1, label='GDP_2022(M)')
bar1_2 = axes[0].barh([p + bar_height for p in index1], merged_industrycontribution['GDP_2023(M)'], bar_height, alpha=opacity, color=color2, label='GDP_2023(M)')
axes[0].set_ylabel('')
axes[0].set_xlabel('GDP Contribution (Millions)')
axes[0].set_title('GDP Contribution by Industry for 2022 and 2023')
axes[0].set_yticks([p + bar_height / 2 for p in index1])
axes[0].set_yticklabels(merged_industrycontribution['Industry'])
axes[0].legend()
# the secondοΌEly Contribution by Industry for 2022 and 2023
index2 = range(len(merged_industrycontribution['Industry']))
bar2_1 = axes[1].barh(index2, merged_industrycontribution['Employment_2022'], bar_height, alpha=opacity, color=color1, label='Employment_2022')
bar2_2 = axes[1].barh([p + bar_height for p in index2], merged_industrycontribution['Employment_2023'], bar_height, alpha=opacity, color=color2, label='Employment_2023')
axes[1].set_ylabel('')
axes[1].set_xlabel('Employment Contribution (Millions)')
axes[1].set_title('Employment Contribution by Industry for 2022 and 2023')
axes[1].set_yticks([p + bar_height / 2 for p in index2])
axes[1].set_yticklabels(merged_industrycontribution['Industry'])
axes[1].legend()
plt.tight_layout()
plt.show()
We can identify several key points from the chart:
The 'Professional, scientific and technical services' sector shows a significant growth trend, both in terms of its contribution to GDP and employment. This suggests that this sector is playing an increasingly important role in the New Zealand economy, probably due to technological innovation, increased demand and policy support.
Although the manufacturing sector's contribution to GDP is declining, its contribution to employment is increasing. This may indicate that the manufacturing sector is undergoing structural adjustment, or that automation technology has not yet fully replaced manual labour. It may also reflect government measures to protect jobs.
Some sectors, such as wholesale and retail trade, have relatively small contributions to GDP but relatively large contributions to employment. This suggests that these sectors are likely to be labour intensive and important in supporting the labour market, although the economic value of individual jobs in these sectors is relatively low.t trade.
1.1.2 Job profiles datasetsΒΆ
(1) What is the data
The Job Profiles datasets are a comprehensive *database provided by Careers.govt.nz* that provides detailed information on various occupations, including *job titles, salary levels, required skills and training duration*. These profiles are based on research and analysis of the New Zealand labour market and aim to provide comprehensive career information for job seekers, students and career planners.
(2) How the data was obtained:
Data crawled from: https://www.careers.govt.nz/searchresults?tab=jobs.
- As the career information is spread over several pages, the page-flipping crawling is implemented using loops and page parameters. In each loop, the program calculates the starting position of each page, constructs the URL and sends a request to retrieve the page content.
- At the end of each loop, the program adds a 1 second delay to avoid overloading the server.
- Then, modules containing career information were extracted from each page and parsed for key information such as title, subtitle, description, employment opportunities and training required.
- Finally, the extracted data is organised into a list and then converted into a DataFrame for subsequent analysis.
(3) What to do with this dataset
Check dataset integrity and clean data. Extract relevant GDP and employment contribution columns from the original tables, then merge for further analysis.
# URL
base_url = "https://www.careers.govt.nz/searchresults?tab=jobs"
total_pages = 42 # Total pages
data = []
for page in range(total_pages):
# Calculate the start position of each page
start_index = page * 10
# Building URLs for paging
url = f"{base_url}&start={start_index}"
# Request page
response = requests.get(url)
html_content = response.text
# Parsing HTML
soup = BeautifulSoup(html_content, 'html.parser')
# Iterate over all article tags
for article in soup.find_all('article', class_='careers-card job'):
title = article.find('h3', class_='title').text.strip()
sub_title = article.find('p', class_='sub-title').text.strip()
description = article.find('div', class_='description').p.text.strip()
job_opportunities = article.find('dl', class_='job-opportunities').dd.text.strip()
training_required = article.find('dl', class_='training').dd.text.strip() if article.find('dl', class_='training') else 'N/A'
vocations = [li['title'] for li in article.find_all('li')]
earnings = [span.text.strip() for span in article.find_all('span', class_='amount')]
# Organising data
data.append({
'Title': title,
'Sub Title': sub_title,
'Description': description,
'Job Opportunities': job_opportunities,
'Training Required': training_required,
'Vocations': ", ".join(vocations),
'Earnings': ", ".join(earnings)
})
# 1 second delay after each cycle
time.sleep(1)
# Create a DataFrame
df_jobprofiles = pd.DataFrame(data)
df_jobprofiles.head()
| Title | Sub Title | Description | Job Opportunities | Training Required | Vocations | Earnings | |
|---|---|---|---|---|---|---|---|
| 0 | Energy/βCarbon Auditor | KaitΔtari PΕ«ngao/βWaro | Energy/carbon auditors assess the amount of en... | Average | 2-3 years | Construction and infrastructure, Services indu... | 80K per year, 200K per year |
| 1 | Television Presenter | KaipΔnui Pouaka Whakaata | Television presenters introduce, present or ho... | Poor | N/A | Creative industries | |
| 2 | Facilities Manager | Kaiwhakahaere Whakaurunga | Facilities managers co-ordinate the strategic ... | Good | N/A | Construction and infrastructure | 100K per year, 150K per year |
| 3 | Aircraft Refueller | KaiwhakakΔ« Waka Rererangi | Aircraft refuellers fill aircraft with fuel at... | Poor | <1 year | Services industries | 65K per year, 75K per year |
| 4 | Electronics Engineer | Mataaro TΔhiko | Electronics engineers design and oversee produ... | Good | 4 years | Manufacturing and technology | $100K per year |
# descriptive statistics of the dataset
df_jobprofiles.describe()
| Title | Sub Title | Description | Job Opportunities | Training Required | Vocations | Earnings | |
|---|---|---|---|---|---|---|---|
| count | 417 | 417 | 417 | 417 | 417 | 417 | 417 |
| unique | 417 | 414 | 417 | 3 | 39 | 36 | 318 |
| top | Energy/βCarbon Auditor | Ringawera | Energy/carbon auditors assess the amount of en... | Good | N/A | Services industries | |
| freq | 1 | 2 | 1 | 228 | 146 | 99 | 26 |
# Rename Columns
df_jobprofiles.rename(columns={
'Vocations': 'Industry',
'Title': 'Occupation',
}, inplace=True)
Based on the statistics in the table above, it can be found:
- 'Title' column: with 417 unique values, *the title of each job is unique*, indicating that our dataset has a high degree of diversity in terms of job types.
- 'Job opportunities' column: *'Good' appears 228 times*, which is the most common rating for job opportunities. This suggests that many occupations have good job prospects in the market.
- 'Training required' column: 'N/A' appears 146 times. This indicates that *a large number of occupation entries don't list the required training requirements*, which may affect the comprehensiveness of the occupations assessed.
- 'Earnings' column: There were 26 records where no specific earnings information was provided.
To ensure the completeness and reliability of the data analysis, it is important to *select columns that do not have null values*.
jobprofiles = df_jobprofiles[['Occupation', 'Job Opportunities','Industry']].copy()
jobprofiles.head()
| Occupation | Job Opportunities | Industry | |
|---|---|---|---|
| 0 | Energy/βCarbon Auditor | Average | Construction and infrastructure, Services indu... |
| 1 | Television Presenter | Poor | Creative industries |
| 2 | Facilities Manager | Good | Construction and infrastructure |
| 3 | Aircraft Refueller | Poor | Services industries |
| 4 | Electronics Engineer | Good | Manufacturing and technology |
For the table provided, it is necessary to *count the number of job vacancies* for each industry.
First of all, it's noticed that some values in the column 'Industry' contain several categories of industries. This requires us to *split each industry information and rebuild the list*. Specifically, we use the .str.split(', ') method to split the string in each cell by commas into lists, and treat each list as a new row.
Next, we use the pd.crosstab() method to create a *cross-tabulation*, taking the Industry column and the Job Opportunities column as parameters. This will calculate the frequency of each industry in relation to each job opportunity and store the result in a new DataFrame.
Finally, by *summing the columns in the cross-tabulation*, we calculate the total number of each job opportunity for further analysis and visualisation.
# Split multiple occupational categories in the βVocationsβ column and then flatten the multi-category structure.
jobprofiles['Industry'] = jobprofiles['Industry'].str.split(', ')
jobprofiles = jobprofiles.explode('Industry').reset_index(drop=True)
# Creating a Crosstab with crosstab
summary_table = pd.crosstab(jobprofiles['Industry'], jobprofiles['Job Opportunities'])
# Calculate the total number of ratings for each job opportunity
total_job_opportunities = summary_table.sum()
summary_table
| Job Opportunities | Average | Good | Poor |
|---|---|---|---|
| Industry | |||
| Construction and infrastructure | 16 | 40 | 3 |
| Creative industries | 22 | 7 | 29 |
| Manufacturing and technology | 25 | 64 | 11 |
| Primary industries | 22 | 34 | 3 |
| Services industries | 61 | 75 | 30 |
| Social and community services | 29 | 66 | 15 |
1.2 Static datasetsΒΆ
Focusing on the *labour market* and the *economic climate*, a wide range of data was gathered.
- Job market
- *Online job data*: All Vacancies Indices (AVI) published by the Ministry of Business, Innovation and Employment (MBIE) covering the period 2010 to 2024. This dataset includes various dimensions such as industry, occupation, region and skills.
- *Regional employment data*: Quarterly data from 1999 to 2022, providing employment statistics for different regions and industries in New Zealand, including job creation and destruction.
- *Labour Force Status datasets*: including measures such as employment, unemployment and participation rates.
- *Median Wage*: Distribution of earnings across different age groups
- Economic climate:
GDP is a key indicator of economic activity and performance. Two key datasets have been selected
- *GDP by region*: provides insight into regional economic performance
- *GDP by industry*: used to assess the sectoral contribution to overall economic growth.
1.2.1 Job marketΒΆ
1.2.1.1 Job online datasetΒΆ
Jobs Online provides a key indicator for assessing labour demand by tracking fluctuations in online job postings across four prominent internet job platforms: SEEK, Trade Me Jobs, the Education Gazette and Kiwi Health Jobs.The data can be used as a proxy indicator for job vacancies and can provide insight into trends across industries, occupations, skill levels and geographic regions.
# import data
file_path = "../AAA/datasets/jobs-online-all.xlsx"
# specify the first two rows as a multilevel column index
df_jobonline_all = pd.read_excel(file_path, header=[0, 1])
df_jobonline_all.head()
| Date | Auckland | Bay of Plenty | Canterbury | Gisborne Hawke's Bay | Marlborough/NelsonTasman/West Coast | Manawatu/Wanganui-Taranaki | Northland | Otago Southland | Waikato | ... | OCCUPATION | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Unnamed: 0_level_1 | Unnamed: 1_level_1 | Unnamed: 2_level_1 | Unnamed: 3_level_1 | Unnamed: 4_level_1 | Unnamed: 5_level_1 | Unnamed: 6_level_1 | Unnamed: 7_level_1 | Unnamed: 8_level_1 | Unnamed: 9_level_1 | ... | Sales | Machinery Drivers | Labourers | highly skilled | skilled | semi-skilled | low skilled | unskilled | Skilled | Unskilled | |
| 0 | NaT | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 1 | NaT | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 2 | 2009-09-01 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 3 | 2009-12-01 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 4 | 2010-03-01 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
5 rows Γ 36 columns
#View all column names
columns = df_jobonline_all.columns.tolist()
print("Columns in the dataset:", columns)
Columns in the dataset: [('Date', 'Unnamed: 0_level_1'), ('Auckland', 'Unnamed: 1_level_1'), ('Bay of Plenty', 'Unnamed: 2_level_1'), ('Canterbury', 'Unnamed: 3_level_1'), ("Gisborne Hawke's Bay", 'Unnamed: 4_level_1'), ('Marlborough/NelsonTasman/West Coast', 'Unnamed: 5_level_1'), ('Manawatu/Wanganui-Taranaki', 'Unnamed: 6_level_1'), ('Northland', 'Unnamed: 7_level_1'), ('Otago Southland', 'Unnamed: 8_level_1'), ('Waikato', 'Unnamed: 9_level_1'), ('Wellington', 'Unnamed: 10_level_1'), ('Industry', 'Accounting'), ('Industry', 'Construction'), ('Industry', 'Education'), ('Industry', 'Health'), ('Industry', 'Hospitalty'), ('Industry', 'IT'), ('Industry', 'Manufacturing'), ('Industry', 'Primary'), ('Industry', 'Sales'), ('Industry', 'Other'), ('OCCUPATION', 'Managers'), ('OCCUPATION', 'Professionals'), ('OCCUPATION', 'Tech & Trades'), ('OCCUPATION', 'Community & Personal Services'), ('OCCUPATION', 'Clerical & Administration'), ('OCCUPATION', 'Sales'), ('OCCUPATION', 'Machinery Drivers'), ('OCCUPATION', 'Labourers'), ('OCCUPATION', 'highly skilled'), ('OCCUPATION', 'skilled'), ('OCCUPATION', 'semi-skilled'), ('OCCUPATION', 'low skilled'), ('OCCUPATION', 'unskilled'), ('OCCUPATION', 'Skilled'), ('OCCUPATION', 'Unskilled')]
The column names in this dataset have multi-level indexes and therefore need to be adjusted.
- For column names related to regions, only the name of each region will be kept and redundant rows will be removed.
- For column names related to industries, if the first part of the column name is βIndustryβ, the second part of the column name will be added to a new list of column names. In addition, the prefix βids_β will be added to indicate that these are industry-related columns.
- For occupation-related columns, if the first part of the column name is βOCCUPATIONβ, the second part of the column name will be added to a new list of column names. Again, the prefix βocp_β will be added to indicate that these are occupation-related columns.
# Processing column names
new_columns = []
for col in df_jobonline_all.columns:
# Check and process region names
if "Unnamed" in str(col[1]):
new_columns.append(col[0])
# Dealing with Industry-related columns
elif col[0] == 'Industry':
new_columns.append(f'ids_{col[1]}')
# Dealing with Occupation-related columns
elif col[0] == 'OCCUPATION':
new_columns.append(f'ocp_{col[1]}')
else:
new_columns.append(col)
# update column names
df_jobonline_all.columns = new_columns
print("Updated columns:", df_jobonline_all.columns)
Updated columns: Index(['Date', 'Auckland', 'Bay of Plenty', 'Canterbury',
'Gisborne Hawke's Bay', 'Marlborough/NelsonTasman/West Coast',
'Manawatu/Wanganui-Taranaki', 'Northland', 'Otago Southland', 'Waikato',
'Wellington', 'ids_Accounting', 'ids_Construction', 'ids_Education',
'ids_Health', 'ids_Hospitalty', 'ids_IT', 'ids_Manufacturing',
'ids_Primary', 'ids_Sales', 'ids_Other', 'ocp_Managers',
'ocp_Professionals', 'ocp_Tech & Trades',
'ocp_Community & Personal Services', 'ocp_Clerical & Administration',
'ocp_Sales', 'ocp_Machinery Drivers', 'ocp_Labourers',
'ocp_highly skilled', 'ocp_skilled', 'ocp_semi-skilled',
'ocp_low skilled', 'ocp_unskilled', 'ocp_Skilled', 'ocp_Unskilled'],
dtype='object')
As can be seen from the above results, the column names have been successfully updated.
Since it is observed that there are blank rows at the beginning of this data frame, it is necessary to check and address any missing values in this data set.
The next step was to count the number of missing values in each row.
from IPython.display import display, HTML
# Calculate the number of missing values for each row
missing_counts = df_jobonline_all.isna().sum(axis=1)
# Calculate the total number of columns in each row
total_columns = df_jobonline_all.shape[1]
# Calculate the percentage of missing values in each row
missing_percentage = (missing_counts / total_columns) * 100
# Create a new DataFrame showing the number and percentage of missing values in each row
missing_stats = pd.DataFrame({
'Missing Values Count': missing_counts,
'Missing Percentage': missing_percentage
})
# Setting up display configurations to avoid content being omitted
pd.set_option('display.max_rows', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.width', None)
pd.set_option('display.max_colwidth', None)
# Use IPython's display function with HTML to create a scrolling viewframe to display the DataFrame.
display(HTML(missing_stats.to_html()))
| Missing Values Count | Missing Percentage | |
|---|---|---|
| 0 | 36 | 100.000000 |
| 1 | 36 | 100.000000 |
| 2 | 35 | 97.222222 |
| 3 | 35 | 97.222222 |
| 4 | 35 | 97.222222 |
| 5 | 35 | 97.222222 |
| 6 | 35 | 97.222222 |
| 7 | 0 | 0.000000 |
| 8 | 0 | 0.000000 |
| 9 | 0 | 0.000000 |
| 10 | 0 | 0.000000 |
| 11 | 0 | 0.000000 |
| 12 | 0 | 0.000000 |
| 13 | 0 | 0.000000 |
| 14 | 0 | 0.000000 |
| 15 | 0 | 0.000000 |
| 16 | 0 | 0.000000 |
| 17 | 0 | 0.000000 |
| 18 | 0 | 0.000000 |
| 19 | 0 | 0.000000 |
| 20 | 0 | 0.000000 |
| 21 | 0 | 0.000000 |
| 22 | 0 | 0.000000 |
| 23 | 0 | 0.000000 |
| 24 | 0 | 0.000000 |
| 25 | 0 | 0.000000 |
| 26 | 0 | 0.000000 |
| 27 | 0 | 0.000000 |
| 28 | 0 | 0.000000 |
| 29 | 0 | 0.000000 |
| 30 | 0 | 0.000000 |
| 31 | 0 | 0.000000 |
| 32 | 0 | 0.000000 |
| 33 | 0 | 0.000000 |
| 34 | 0 | 0.000000 |
| 35 | 0 | 0.000000 |
| 36 | 0 | 0.000000 |
| 37 | 0 | 0.000000 |
| 38 | 0 | 0.000000 |
| 39 | 0 | 0.000000 |
| 40 | 0 | 0.000000 |
| 41 | 0 | 0.000000 |
| 42 | 0 | 0.000000 |
| 43 | 0 | 0.000000 |
| 44 | 0 | 0.000000 |
| 45 | 0 | 0.000000 |
| 46 | 0 | 0.000000 |
| 47 | 0 | 0.000000 |
| 48 | 0 | 0.000000 |
| 49 | 0 | 0.000000 |
| 50 | 0 | 0.000000 |
| 51 | 0 | 0.000000 |
| 52 | 0 | 0.000000 |
| 53 | 0 | 0.000000 |
| 54 | 0 | 0.000000 |
| 55 | 0 | 0.000000 |
| 56 | 0 | 0.000000 |
| 57 | 0 | 0.000000 |
| 58 | 0 | 0.000000 |
| 59 | 0 | 0.000000 |
| 60 | 0 | 0.000000 |
From the statistical results, it is evident that all missing values are located in the first 6 rows.
Therefore, drop the first 6 rows of this dataset.
# Delete lines 0 to 6
df_jobonline_all = df_jobonline_all.drop(index=range(7))
df_jobonline_all.head()
| Date | Auckland | Bay of Plenty | Canterbury | Gisborne Hawke's Bay | Marlborough/NelsonTasman/West Coast | Manawatu/Wanganui-Taranaki | Northland | Otago Southland | Waikato | Wellington | ids_Accounting | ids_Construction | ids_Education | ids_Health | ids_Hospitalty | ids_IT | ids_Manufacturing | ids_Primary | ids_Sales | ids_Other | ocp_Managers | ocp_Professionals | ocp_Tech & Trades | ocp_Community & Personal Services | ocp_Clerical & Administration | ocp_Sales | ocp_Machinery Drivers | ocp_Labourers | ocp_highly skilled | ocp_skilled | ocp_semi-skilled | ocp_low skilled | ocp_unskilled | ocp_Skilled | ocp_Unskilled | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 7 | 2010-12-01 | 100.000 | 100.000 | 100.000 | 100.000 | 100.000 | 100.000 | 100.000 | 100.000 | 100.000 | 100.000 | 100.000 | 100.000 | 100.000 | 100.000 | 100.000 | 100.000 | 100.000 | 100.000 | 100.000 | 100.000 | 100.000 | 100.000 | 100.000 | 100.000 | 100.000 | 100.000 | 100.000 | 100.000 | 100.000 | 100.000 | 100.000 | 100.000 | 100.000 | 100.000 | 100.000 |
| 8 | 2011-03-01 | 101.457 | 101.561 | 107.036 | 103.535 | 102.370 | 100.780 | 103.643 | 106.457 | 102.388 | 101.849 | 101.830 | 107.252 | 96.481 | 98.957 | 104.711 | 101.374 | 103.521 | 108.968 | 100.481 | 102.091 | 101.755 | 102.361 | 110.413 | 107.814 | 101.682 | 104.106 | 102.947 | 109.816 | 102.438 | 101.859 | 113.548 | 105.121 | 102.011 | 104.132 | 104.480 |
| 9 | 2011-06-01 | 101.942 | 102.781 | 129.833 | 106.559 | 105.940 | 102.055 | 108.489 | 111.892 | 105.167 | 102.005 | 105.035 | 114.110 | 92.110 | 101.100 | 108.760 | 102.307 | 109.045 | 118.109 | 101.360 | 106.037 | 103.000 | 105.634 | 119.675 | 114.379 | 105.660 | 107.007 | 109.098 | 116.151 | 105.300 | 102.154 | 125.963 | 110.445 | 103.550 | 107.945 | 109.077 |
| 10 | 2011-09-01 | 101.542 | 104.443 | 139.557 | 109.635 | 114.017 | 104.820 | 113.102 | 119.014 | 107.132 | 100.886 | 108.501 | 118.782 | 91.165 | 105.883 | 108.103 | 102.536 | 115.264 | 129.535 | 102.664 | 112.256 | 104.741 | 108.902 | 125.667 | 117.779 | 110.105 | 106.261 | 117.874 | 120.426 | 107.653 | 104.015 | 132.978 | 113.728 | 104.835 | 110.780 | 112.078 |
| 11 | 2011-12-01 | 101.208 | 106.354 | 150.820 | 111.782 | 122.670 | 108.420 | 112.774 | 126.815 | 107.699 | 100.552 | 109.625 | 122.864 | 89.039 | 111.135 | 107.003 | 102.640 | 117.823 | 138.805 | 103.434 | 115.405 | 106.173 | 109.886 | 128.853 | 121.391 | 111.857 | 103.751 | 123.715 | 126.862 | 108.200 | 106.747 | 135.339 | 114.841 | 107.073 | 111.802 | 113.392 |
import matplotlib.pyplot as plt
# Select columns starting with βocp_β to plot
ocp_columns = [col for col in df_jobonline_all.columns if col.startswith('ocp_')]
ocp_data = df_jobonline_all[ocp_columns]
plt.figure(figsize=(20, 6))
# plotting the 1st graph
# Set the diagram to the first position in a row of three columns
plt.subplot(1, 3, 1)
for column in ocp_columns:
plt.plot(df_jobonline_all['Date'], df_jobonline_all[column], marker=None, linestyle='-', label=column)
# Add legend and remove bottom frame
plt.legend(frameon=False)
# Select columns starting with βids_β to plot
ids_columns = [col for col in df_jobonline_all.columns if col.startswith('ids_')]
ids_data = df_jobonline_all[ids_columns]
# plotting the 2nd graph
plt.subplot(1, 3, 2)
for column in ids_columns:
plt.plot(df_jobonline_all['Date'], df_jobonline_all[column], marker=None, linestyle='-', label=column)
plt.legend(frameon=False)
# Selecting variables to plot other than βDateβ and the above two groups of variables
other_columns = [col for col in df_jobonline_all.columns if col != 'Date' and col not in ocp_columns and col not in ids_columns]
other_data = df_jobonline_all[other_columns]
# plotting the 3rd graph
plt.subplot(1, 3, 3)
for column in other_columns:
plt.plot(df_jobonline_all['Date'], df_jobonline_all[column], marker=None, linestyle='-', label=column)
plt.legend(frameon=False)
plt.tight_layout()
plt.show()
Data clean result:
- These graphs show that as *time series data, the variables exhibit smooth curves, indicating the absence of outliers. In addition, there is no discontinuity in the curves, indicating that there are no missing values* in the dataset. Therefore, data cleaning was completed for this dataset.
Insights from the charts:
- Also, we have some findings from the graphs.The data from 2020 onwards show a significant decline, mainly due to the contraction of the labour market caused by COVID-19. In 2022, there is a rapid recovery in the labour market and a peak in recruitment demand. However, there is a decline in market demand from 2023 onwards, suggesting that this is a normal adjustment after a brief recovery. The market is still in a downward trend, showing a continuous decline in demand for jobs.
1.2.1.2 Regional Employment DataΒΆ
The dataset contains quarterly employment data from 1999 to 2022, providing insight into employment trends across New Zealand's different regions and industries.
In addition, the dataset contains information on *job creation and destruction*, allowing detailed analysis of the dynamics of employment over time.
# import data
regionjob = pd.read_csv('../AAA/datasets/regionjob.csv')
regionjob.head()
| Date | Type | Auckland | Waikato | Bay of Plenty | Gisborne | Hawke's Bay | Taranaki | Manawatu-Wanganui | Wellington | Tasman, Nelson, Marlborough, West Coast | Canterbury | Otago | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1999Q2 | Job Creation | 41890 | 5570 | 3430 | 520 | 2040 | 1560 | 3480 | 20430 | 1900 | 9430 | 3070 |
| 1 | 1999Q3 | Job Creation | 2710 | 340 | 370 | 35 | 120 | 90 | 170 | 1080 | 130 | 670 | 170 |
| 2 | 1999Q4 | Job Creation | 3040 | 500 | 260 | 30 | 150 | 120 | 220 | 1100 | 100 | 820 | 220 |
| 3 | 2000Q1 | Job Creation | 3340 | 610 | 420 | 45 | 210 | 210 | 240 | 1530 | 130 | 750 | 210 |
| 4 | 2000Q2 | Job Creation | 3700 | 760 | 400 | 25 | 210 | 180 | 260 | 1820 | 150 | 1140 | 380 |
regionjob.describe()
| Auckland | Waikato | Bay of Plenty | Gisborne | Hawke's Bay | Taranaki | Manawatu-Wanganui | Wellington | Tasman, Nelson, Marlborough, West Coast | Canterbury | Otago | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 190.000000 | 190.000000 | 190.000000 | 190.000000 | 190.000000 | 190.000000 | 190.000000 | 190.000000 | 190.000000 | 190.000000 | 190.000000 |
| mean | 4237.473684 | 614.368421 | 453.263158 | 55.221053 | 214.131579 | 150.921053 | 250.315789 | 1511.000000 | 208.473684 | 1066.684211 | 327.736842 |
| std | 2954.425062 | 405.542776 | 277.856276 | 41.516537 | 148.068369 | 110.653753 | 245.363148 | 1433.490412 | 138.220118 | 697.969129 | 222.624162 |
| min | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 25% | 3307.500000 | 462.500000 | 320.000000 | 35.000000 | 160.000000 | 110.000000 | 190.000000 | 1110.000000 | 160.000000 | 782.500000 | 250.000000 |
| 50% | 3930.000000 | 560.000000 | 400.000000 | 50.000000 | 200.000000 | 140.000000 | 220.000000 | 1400.000000 | 190.000000 | 965.000000 | 300.000000 |
| 75% | 4727.500000 | 690.000000 | 497.500000 | 65.000000 | 230.000000 | 170.000000 | 260.000000 | 1617.500000 | 237.500000 | 1200.000000 | 380.000000 |
| max | 41890.000000 | 5570.000000 | 3430.000000 | 520.000000 | 2040.000000 | 1560.000000 | 3480.000000 | 20430.000000 | 1900.000000 | 9430.000000 | 3070.000000 |
From the descriptive statistics analysis, it is evident that the minimum value of 0 for job creation is not justified.
In addition, the maximum value is significantly above the 75% percentile, indicating the presence of outliers in the data set.
Further exploration of the distribution is necessary to identify and analyse these outliers.
import pandas as pd
import matplotlib.pyplot as plt
# Selection of variables from columns 3 to 14
selected_columns = regionjob.iloc[:, 2:14]
# Setting the graphic size
plt.figure(figsize=(15, 10))
# Iterate through each column and plot a bar graph
for i, column in enumerate(selected_columns.columns):
plt.subplot(3, 4, i + 1)
plt.bar(regionjob.index, selected_columns[column], color='blue', edgecolor='none')
plt.xlabel('Year')
plt.ylabel(column)
plt.title(column)
plt.tight_layout()
plt.show()
From the above visualisation, it is clear that the outliers are mainly concentrated in the initial data point, that is the first row of the dataset.
In addition, in the βTrend of Hawke's Bayβ graph, there is an outlier in the middle region with a value of about 600, which is higher than the average value of 214. Considering that this outlier is an indicator of job creation, the magnitude of its change may be related to the changes in the economic environment at that time. Therefore, we decided not to treat it as an outlier but to keep it in the data set.
Next, move on to dealing with the 0 values in the dataset.
# Count the number and location of zeros for each variable
zero_counts = (regionjob == 0).sum()
zero_locations = {col: regionjob.index[regionjob[col] == 0].tolist() for col in regionjob.columns}
print("Number of zeros for each variable:")
print(zero_counts)
print("\nThe location of the zero value of each variable:")
for col, locations in zero_locations.items():
print(f"{col}: {locations}")
Number of zeros for each variable: Date 0 Type 0 Auckland 1 Waikato 1 Bay of Plenty 1 Gisborne 1 Hawke's Bay 1 Taranaki 1 Manawatu-Wanganui 1 Wellington 1 Tasman, Nelson, Marlborough, West Coast 1 Canterbury 1 Otago 1 dtype: int64 The location of the zero value of each variable: Date: [] Type: [] Auckland: [95] Waikato: [95] Bay of Plenty: [95] Gisborne: [95] Hawke's Bay: [95] Taranaki: [95] Manawatu-Wanganui: [95] Wellington: [95] Tasman, Nelson, Marlborough, West Coast: [95] Canterbury: [95] Otago: [95]
#View data at index [95]
print(regionjob.iloc[95])
Date 1999Q2 Type Job Destruction Auckland 0 Waikato 0 Bay of Plenty 0 Gisborne 0 Hawke's Bay 0 Taranaki 0 Manawatu-Wanganui 0 Wellington 0 Tasman, Nelson, Marlborough, West Coast 0 Canterbury 0 Otago 0 Name: 95, dtype: object
From the results above, it is noticed that the outliers and zeros are concentrated in the time period 1999Q2.
Therefore, the data for that time point is required to be deleted.
regionjob = regionjob[regionjob['Date'] != '1999Q2']
regionjob.head()
| Date | Type | Auckland | Waikato | Bay of Plenty | Gisborne | Hawke's Bay | Taranaki | Manawatu-Wanganui | Wellington | Tasman, Nelson, Marlborough, West Coast | Canterbury | Otago | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 1999Q3 | Job Creation | 2710 | 340 | 370 | 35 | 120 | 90 | 170 | 1080 | 130 | 670 | 170 |
| 2 | 1999Q4 | Job Creation | 3040 | 500 | 260 | 30 | 150 | 120 | 220 | 1100 | 100 | 820 | 220 |
| 3 | 2000Q1 | Job Creation | 3340 | 610 | 420 | 45 | 210 | 210 | 240 | 1530 | 130 | 750 | 210 |
| 4 | 2000Q2 | Job Creation | 3700 | 760 | 400 | 25 | 210 | 180 | 260 | 1820 | 150 | 1140 | 380 |
| 5 | 2000Q3 | Job Creation | 3340 | 430 | 320 | 40 | 180 | 120 | 250 | 1530 | 130 | 690 | 200 |
When we look at changes in the employment environment across regions, we want to analyse them on an annual basis. Therefore, it is necessary to convert quarterly data to annual data. Since the values in the table represent job creation and job destruction for the current period, the data for each quarter can be directly summed up when converting to annual data.
regionjob['Year'] = regionjob['Date'].str.extract(r'(\d{4})').astype(int)
# Aggregate data based on year and type and calculate the sum for each area
regionjob_yearly = regionjob.groupby(['Year', 'Type']).sum().reset_index()
regionjob_yearly.drop(columns='Date', inplace=True, errors='ignore')
regionjob_yearly.head()
| Year | Type | Auckland | Waikato | Bay of Plenty | Gisborne | Hawke's Bay | Taranaki | Manawatu-Wanganui | Wellington | Tasman, Nelson, Marlborough, West Coast | Canterbury | Otago | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1999 | Job Creation | 5750 | 840 | 630 | 65 | 270 | 210 | 390 | 2180 | 230 | 1490 | 390 |
| 1 | 1999 | Job Destruction | 4510 | 720 | 430 | 60 | 275 | 145 | 300 | 2130 | 165 | 990 | 570 |
| 2 | 2000 | Job Creation | 13440 | 2210 | 1420 | 150 | 880 | 630 | 970 | 6050 | 540 | 3290 | 980 |
| 3 | 2000 | Job Destruction | 13200 | 1800 | 1130 | 145 | 750 | 540 | 930 | 5350 | 510 | 3040 | 1070 |
| 4 | 2001 | Job Creation | 15100 | 2190 | 1350 | 165 | 810 | 600 | 850 | 6820 | 590 | 4580 | 1000 |
After converting and summing the quarterly data to annual data, we also need to *pivot* the Type column to adjust its value into the index for classification purposes. This will allow different types of data (such as Job Creation and Job Destruction) to be clearly categorised and compared.
regionjob_pivot = regionjob_yearly.pivot_table(values=['Auckland', 'Waikato', 'Bay of Plenty', 'Gisborne',
"Hawke's Bay", 'Taranaki', 'Manawatu-Wanganui', 'Wellington',
'Tasman, Nelson, Marlborough, West Coast', 'Canterbury', 'Otago',
], index=['Type', 'Year'])
regionjob_pivot.head()
| Auckland | Bay of Plenty | Canterbury | Gisborne | Hawke's Bay | Manawatu-Wanganui | Otago | Taranaki | Tasman, Nelson, Marlborough, West Coast | Waikato | Wellington | ||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Type | Year | |||||||||||
| Job Creation | 1999 | 5750 | 630 | 1490 | 65 | 270 | 390 | 390 | 210 | 230 | 840 | 2180 |
| 2000 | 13440 | 1420 | 3290 | 150 | 880 | 970 | 980 | 630 | 540 | 2210 | 6050 | |
| 2001 | 15100 | 1350 | 4580 | 165 | 810 | 850 | 1000 | 600 | 590 | 2190 | 6820 | |
| 2002 | 14720 | 1680 | 2940 | 190 | 1170 | 850 | 840 | 620 | 620 | 2080 | 5810 | |
| 2003 | 14590 | 1940 | 3270 | 150 | 820 | 1040 | 1140 | 710 | 710 | 2220 | 4830 |
Based on the different types, the curves for 'Job Creation' and 'Job Destruction' are plotted separately in order to see the final distribution of the data.
# Select data for βJob Creationβ and βJob Destructionβ
job_creation = regionjob_pivot.loc['Job Creation']
job_destruction = regionjob_pivot.loc['Job Destruction']
# Plot the time series of βJob Creationβ
plt.figure(figsize=(8, 3))
for column in job_creation.columns:
plt.plot(job_creation.index, job_creation[column], label=column)
plt.title('Job Creation Over Years', fontsize=10)
plt.xticks(rotation=45,fontsize=9)
plt.legend(loc='upper left', bbox_to_anchor=(1,1.03),fontsize=9)
# Set the range of the x-axis to align with the minimum and maximum years of the data
plt.xlim(min(job_creation.index), max(job_creation.index))
plt.show()
# Plot time series of βJob Destructionβ
plt.figure(figsize=(8, 3))
for column in job_destruction.columns:
plt.plot(job_destruction.index, job_destruction[column], label=column)
plt.title('Job Destruction Over Years', fontsize=10)
plt.xticks(rotation=45,fontsize=9)
plt.legend(loc='upper left', bbox_to_anchor=(1,1.03),fontsize=9)
# Set the range of the x-axis to align with the minimum and maximum years of the data
plt.xlim(min(job_destruction.index), max(job_destruction.index))
plt.show()
Data cleaning results:
- There are no obvious breaks or outliers in the time-series data for each region, and the lines are *smooth and continuous*, which indicates that the data have been well handled in the pre-processing stage for possible missing values or outliers.
Insights from the charts:
- Employment varies considerably across New Zealand's regions, with *Auckland, Wellington and Canterbury having the most active labour markets*.
- *Auckland stands out as an economic centre* with volatile labour markets. This may reflect its large size and diverse economy.
1.2.1.3 Labour Force StatusΒΆ
Labour force status refers to the employment status of persons aged 15-64.
This indicator is crucial for measuring the efficiency of the use of human resources in the labour market.
# import data
file_path = "../AAA/datasets/Labour Force Status for people aged 15 to 64 years.csv"
labour_force_status = pd.read_csv(file_path)
labour_force_status.head()
| Year | Not in Labour Force | Working Age Population | Labour Force Participation Rate | Unemployment Rate | Employment Rate | Total Labour Force | |
|---|---|---|---|---|---|---|---|
| 0 | 1986 | .. | .. | .. | .. | .. | .. |
| 1 | 1987 | 545.5 | 2205.3 | 75.3 | 4.2 | 72.1 | 1659.8 |
| 2 | 1988 | 557.1 | 2221.2 | 74.9 | 4.5 | 71.6 | 1664 |
| 3 | 1989 | 592.9 | 2229.4 | 73.4 | 6.4 | 68.7 | 1636.5 |
| 4 | 1990 | 616.8 | 2236.9 | 72.4 | 7.4 | 67.1 | 1620.2 |
Firstly, the βyearβ variable was checked to determine the time scale of the dataset and to verify the accuracy of the βyearβ variable.
# Set graphic style to white background without grid
sns.set(style="white")
# Plot histogram using seaborn
bins = range(int(labour_force_status['Year'].min()), int(labour_force_status['Year'].max()) + 2)
plt.figure(figsize=(8, 4))
ax = sns.histplot(labour_force_status['Year'], bins=bins, kde=False, color='#34a4eb', edgecolor=None)
# Set up graph titles and axis labels
plt.title('Histogram of Year')
ax.set_xlabel('')
plt.ylabel('Frequency',fontsize=9)
# Set up labels to be displayed every 5 years
tick_labels = range(int(labour_force_status['Year'].min()), int(labour_force_status['Year'].max()) + 1, 5)
plt.xticks(tick_labels,fontsize=9)
# Reduce the thickness of the frame lines
for spine in ax.spines.values():
spine.set_linewidth(0.5)
plt.show()
The visual inspection shows that the data for the 'Year' variable are within the expected range.
On this basis, descriptive statistical analysis is carried out on the remaining variables in the dataset.
labour_force_status.describe()
| Year | |
|---|---|
| count | 39.000000 |
| mean | 2005.000000 |
| std | 11.401754 |
| min | 1986.000000 |
| 25% | 1995.500000 |
| 50% | 2005.000000 |
| 75% | 2014.500000 |
| max | 2024.000000 |
Variable "year" counts 39 while all other variables count 38, indicating one missing value for each variable except "year". At first sight, it appears that the anomalous data are present in the row corresponding to the year 1986.
Therefore, the next step is to remove this anomalous row from the data set.
labour_force_status = labour_force_status.iloc[1:]
labour_force_status.head()
| Year | Not in Labour Force | Working Age Population | Labour Force Participation Rate | Unemployment Rate | Employment Rate | Total Labour Force | |
|---|---|---|---|---|---|---|---|
| 1 | 1987 | 545.5 | 2205.3 | 75.3 | 4.2 | 72.1 | 1659.8 |
| 2 | 1988 | 557.1 | 2221.2 | 74.9 | 4.5 | 71.6 | 1664 |
| 3 | 1989 | 592.9 | 2229.4 | 73.4 | 6.4 | 68.7 | 1636.5 |
| 4 | 1990 | 616.8 | 2236.9 | 72.4 | 7.4 | 67.1 | 1620.2 |
| 5 | 1991 | 606.7 | 2259.3 | 73.1 | 8.7 | 66.7 | 1652.6 |
Finally, *kernel density plots* will be generated for each variable as a check on the integrity and basic characteristics of the data set.
# Convert all columns except βYearβ to numeric values
for col in labour_force_status.columns[1:]:
labour_force_status[col] = pd.to_numeric(labour_force_status[col], errors='coerce')
# Set canvas size and subgraph layout
fig, axes = plt.subplots(nrows=2, ncols=4, figsize=(20, 9))
axes = axes.flatten()
# Plot the KDEs for each variable
for i, column in enumerate(labour_force_status.columns):
sns.kdeplot(data=labour_force_status, x=column, ax=axes[i], fill=True)
axes[i].set_title(column)
axes[i].set_xlabel('')
axes[i].set_ylabel('Density')
plt.tight_layout()
plt.show()
Data clean result
- Data continuity: All variables show a continuous distribution with no obvious breaks or unusual jumps.
- Data normality: Most of the variables are approximately symmetric
- Data range: 'Labour force participation rate', 'Unemployment rate' and 'Employment rate' are within the expected percentage range.
The above indicates that the data are well prepared for further analysis.
Insights from the charts
- Labour force participation rate: A concentration between 75% and 80% indicates a high level of labour market activity, suggesting that a large proportion of the working age population is participating in the labour market.
- Unemployment rate: The most common value is around 5%, which is usually considered a relatively healthy state of the labour market.
- Employment rate: Peaks at around 70%, reflecting a relatively stable employment environment.
1.2.1.4 Median wageΒΆ
# import data
file_path = "../AAA/datasets/age median wage.csv"
age_median_wage = pd.read_csv(file_path)
age_median_wage.head()
| Date | 15-19 | 20-24 | 25-29 | 30-34 | 35-39 | 40-44 | 45-49 | 50-54 | 55-59 | 60-64 | 65 | Total All Ages | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1999Q2 | 1610 | 5650 | 7530 | 8050 | 7930 | 7800 | 7920 | 7800 | 7380 | 6630 | 2390 | 7020 |
| 1 | 1999Q3 | 1600 | 5870 | 7860 | 8400 | 8240 | 8080 | 8210 | 8040 | 7590 | 6790 | 2510 | 7300 |
| 2 | 1999Q4 | 2140 | 6320 | 8300 | 8960 | 8900 | 8750 | 8790 | 8600 | 8140 | 7360 | 2930 | 7870 |
| 3 | 2000Q1 | 2210 | 5810 | 7640 | 8230 | 8170 | 8030 | 8090 | 7910 | 7480 | 6750 | 2700 | 7230 |
| 4 | 2000Q2 | 1810 | 5970 | 7880 | 8450 | 8300 | 8150 | 8260 | 8110 | 7650 | 6950 | 2710 | 7390 |
Firstly, the data set needs to be checked for null values.
missing_values_count = age_median_wage.isna().sum()
print(missing_values_count)
Date 0 15-19 0 20-24 0 25-29 0 30-34 0 35-39 0 40-44 0 45-49 0 50-54 0 55-59 0 60-64 0 65 0 Total All Ages 0 dtype: int64
Next step, plot box plots for each variable to see data completeness.
sns.set(style="whitegrid")
age_median_wage_long = pd.melt(age_median_wage, id_vars=['Date'], var_name='Age Group', value_name='Median Wage')
# Plot box plots, including wage data for all age groups
plt.figure(figsize=(10, 5))
ax = sns.boxplot(x='Age Group', y='Median Wage', data=age_median_wage_long)
plt.xticks(rotation=45)
plt.title('Median Wage Distribution by Age Group')
plt.xlabel('')
plt.ylabel('')
plt.show()
Data Wrangling result
- Outlier identification: There are a few outliers in the 15-19 age group, but they do not deviate much from the overall distribution.
- Data consistency: The data are relatively consistent across the age groups, with no obvious data breaks or non-logical distributions, indicating good overall data consistency.
Insight from chart
- Age group 15-19: This group has significantly lower wages than other age groups, in line with the characteristics of first-time or part-time students.
- Ages 15-19 to 35-39: Median wages increase, reflecting the general pattern of career progression with experience and skills.
- Ages 30 to 54: median wages and interquartile range are relatively stable, reflecting a stable career with little change in wages.
- Age 55 and over: Median wages decline, particularly in the 65+ age group, possibly reflecting gradual retirement and reductions in working hours.
For further analysis, it's necessary to *convert the dataset into an annual format*. This is done as follows:
- *Group the data by year*.
- *Calculate the average of all quarters* within each year.
# Extraction year
age_median_wage['Year'] = age_median_wage['Date'].str[:4].astype(int)
# Ensure that all non-date columns are of numeric type
numeric_cols = age_median_wage.select_dtypes(include=[np.number]).columns.tolist()
if 'Year' in numeric_cols:
numeric_cols.remove('Year')
# Group by year and calculate the average of the numerical columns
age_median_wage_year = age_median_wage.groupby('Year',as_index=True)[numeric_cols].mean()
age_median_wage_year = age_median_wage_year.reset_index()
age_median_wage_year.iloc[:, 1:] = age_median_wage_year.iloc[:, 1:].round(1)
age_median_wage_year.head()
| Year | 15-19 | 20-24 | 25-29 | 30-34 | 35-39 | 40-44 | 45-49 | 50-54 | 55-59 | 60-64 | 65 | Total All Ages | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1999 | 1783.3 | 5946.7 | 7896.7 | 8470.0 | 8356.7 | 8210.0 | 8306.7 | 8146.7 | 7703.3 | 6926.7 | 2610.0 | 7396.7 |
| 1 | 2000 | 2017.5 | 6042.5 | 7960.0 | 8575.0 | 8477.5 | 8317.5 | 8390.0 | 8230.0 | 7757.5 | 7035.0 | 2845.0 | 7512.5 |
| 2 | 2001 | 2190.0 | 6190.0 | 8165.0 | 8825.0 | 8755.0 | 8602.5 | 8650.0 | 8507.5 | 8032.5 | 7262.5 | 3282.5 | 7745.0 |
| 3 | 2002 | 2355.0 | 6370.0 | 8392.5 | 9107.5 | 9067.5 | 8947.5 | 8945.0 | 8837.5 | 8385.0 | 7567.5 | 3675.0 | 8002.5 |
| 4 | 2003 | 2505.0 | 6522.5 | 8620.0 | 9397.5 | 9372.5 | 9270.0 | 9275.0 | 9167.5 | 8727.5 | 7900.0 | 4090.0 | 8260.0 |
The median wage dataset has been successfully transformed into an annual format to facilitate further analysis. For the labour market dataset, the pre-processing steps have been completed for
- Online job
- Regional employment
- Labour force status
- Median wagee:
1.2.2 GDP datasetsΒΆ
1.2.2.1 GPD regionΒΆ
# import data
file_path = "../AAA/datasets/GDP_region.csv"
GDP_region = pd.read_csv(file_path)
GDP_region
| Year | Northland | Auckland | Waikato | Bay of Plenty | Gisborne | Hawke's Bay | Taranaki | Manawatu-Whanganui | Wellington | West Coast | Canterbury | Otago | Southland | Marlborough | Tasman/Nelson | Total North Island | Total South Island | New Zealand | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2000 | 2655 | 37716 | 8582 | 5430 | 901 | 3386 | 3595 | 4742 | 16021 | 647 | 12215 | 4265 | 2355 | 935 | 1900 | 83028 | 22317 | 105345 |
| 1 | 2001 | 2954 | 38850 | 9712 | 5843 | 945 | 3643 | 4378 | 5065 | 16250 | 734 | 12849 | 4606 | 2746 | 1025 | 2035 | 87640 | 23995 | 111635 |
| 2 | 2002 | 3188 | 41840 | 10595 | 6238 | 986 | 3882 | 4479 | 5421 | 17424 | 773 | 13795 | 5030 | 2966 | 1135 | 2219 | 94054 | 25919 | 119973 |
| 3 | 2003 | 3082 | 45772 | 10181 | 6405 | 1008 | 4090 | 4344 | 5434 | 18184 | 744 | 14675 | 5347 | 2882 | 1160 | 2300 | 98500 | 27108 | 125607 |
| 4 | 2004 | 3316 | 49398 | 11038 | 6810 | 1020 | 4368 | 4327 | 5782 | 19021 | 818 | 15851 | 5732 | 2994 | 1276 | 2481 | 105079 | 29152 | 134231 |
| 5 | 2005 | 3659 | 53230 | 11666 | 7297 | 1073 | 4690 | 4356 | 6144 | 19957 | 862 | 17328 | 6198 | 2972 | 1351 | 2661 | 112073 | 31372 | 143444 |
| 6 | 2006 | 3969 | 55823 | 12516 | 7664 | 1087 | 4814 | 4518 | 6590 | 21190 | 1002 | 18272 | 6463 | 3163 | 1429 | 2653 | 118170 | 32982 | 151152 |
| 7 | 2007 | 4345 | 57384 | 13869 | 8399 | 1170 | 4946 | 5287 | 6695 | 21615 | 1075 | 19368 | 6807 | 3373 | 1584 | 2864 | 123709 | 35070 | 158779 |
| 8 | 2008 | 4619 | 61743 | 14708 | 9110 | 1211 | 4854 | 7649 | 7245 | 23220 | 1260 | 20901 | 7322 | 3873 | 1802 | 3047 | 134359 | 38204 | 172564 |
| 9 | 2009 | 4734 | 61098 | 15526 | 9039 | 1285 | 5061 | 7924 | 7165 | 23816 | 1388 | 21611 | 7577 | 4123 | 1851 | 3166 | 135648 | 39715 | 175363 |
| 10 | 2010 | 4543 | 63382 | 15258 | 9409 | 1324 | 5150 | 7665 | 7544 | 25113 | 1351 | 22229 | 7894 | 4168 | 1794 | 3296 | 139388 | 40731 | 180119 |
| 11 | 2011 | 4842 | 65759 | 16075 | 9947 | 1394 | 5447 | 7977 | 7888 | 25501 | 1427 | 23219 | 8171 | 4498 | 1813 | 3446 | 144830 | 42574 | 187404 |
| 12 | 2012 | 4922 | 68978 | 17102 | 10321 | 1432 | 5575 | 7896 | 7997 | 26724 | 1517 | 24316 | 8402 | 4599 | 1919 | 3503 | 150947 | 44256 | 195203 |
| 13 | 2013 | 4899 | 71351 | 16725 | 10490 | 1427 | 5730 | 7967 | 7999 | 27149 | 1449 | 25371 | 8526 | 4432 | 2001 | 3581 | 153739 | 45360 | 199099 |
| 14 | 2014 | 5363 | 75444 | 18690 | 11030 | 1474 | 6044 | 8508 | 8558 | 28348 | 1548 | 28198 | 9224 | 5075 | 2224 | 3873 | 163457 | 50143 | 213600 |
| 15 | 2015 | 5555 | 81076 | 18754 | 11291 | 1541 | 6176 | 8329 | 8681 | 29588 | 1479 | 29434 | 9427 | 4841 | 2338 | 3996 | 170990 | 51516 | 222506 |
| 16 | 2016 | 5994 | 88139 | 19413 | 12287 | 1638 | 6444 | 7221 | 8944 | 30654 | 1380 | 30338 | 10057 | 4693 | 2440 | 4216 | 180735 | 53124 | 233859 |
| 17 | 2017 | 6428 | 93624 | 20709 | 13820 | 1707 | 7011 | 7279 | 9358 | 32240 | 1450 | 31189 | 10745 | 5122 | 2604 | 4492 | 192176 | 55602 | 247778 |
| 18 | 2018 | 6970 | 99794 | 22583 | 15106 | 1844 | 7567 | 8129 | 10123 | 33610 | 1632 | 33555 | 11754 | 5609 | 2798 | 4835 | 205727 | 60184 | 265911 |
| 19 | 2019 | 7238 | 105433 | 24349 | 16322 | 1999 | 7886 | 8245 | 10744 | 34849 | 1685 | 35066 | 12484 | 5932 | 2966 | 5162 | 217065 | 63295 | 280360 |
| 20 | 2020 | 7712 | 110312 | 25993 | 17283 | 2056 | 8397 | 8483 | 11344 | 37561 | 1725 | 37099 | 13097 | 6093 | 3135 | 5487 | 229139 | 66638 | 295777 |
| 21 | 2021 | 7804 | 113290 | 26954 | 18012 | 2256 | 8878 | 8077 | 12115 | 37606 | 1784 | 37011 | 12860 | 6180 | 3197 | 5582 | 234991 | 66613 | 301604 |
| 22 | 2022 | 8684 | 124270 | 29378 | 19527 | 2318 | 9773 | 8911 | 13110 | 40777 | 1798 | 40380 | 14016 | 7144 | 3283 | 6038 | 256748 | 72659 | 329407 |
| 23 | 2023 | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. |
GDP_region.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 24 entries, 0 to 23 Data columns (total 19 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Year 24 non-null int64 1 Northland 24 non-null object 2 Auckland 24 non-null object 3 Waikato 24 non-null object 4 Bay of Plenty 24 non-null object 5 Gisborne 24 non-null object 6 Hawke's Bay 24 non-null object 7 Taranaki 24 non-null object 8 Manawatu-Whanganui 24 non-null object 9 Wellington 24 non-null object 10 West Coast 24 non-null object 11 Canterbury 24 non-null object 12 Otago 24 non-null object 13 Southland 24 non-null object 14 Marlborough 24 non-null object 15 Tasman/Nelson 24 non-null object 16 Total North Island 24 non-null object 17 Total South Island 24 non-null object 18 New Zealand 24 non-null object dtypes: int64(1), object(18) memory usage: 3.7+ KB
Based on the above information, it is known that this dataset contains a total of 24 data entries and that there are no null values. However, there is an anomalous character '...' in the data for the year 2023. In order to further process this anomalous data, we need to take the following steps:
- Replacement of the anomalous character '...' with a null value for subsequent data imputation
- Plot the trend
- Selection of the data imputation method based on the trend
# Replace β...β for NaN
GDP_region.replace('..', pd.NA, inplace=True)
GDP_region = GDP_region.apply(pd.to_numeric, errors='coerce')
print(GDP_region.info())
<class 'pandas.core.frame.DataFrame'> RangeIndex: 24 entries, 0 to 23 Data columns (total 19 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Year 24 non-null int64 1 Northland 23 non-null float64 2 Auckland 23 non-null float64 3 Waikato 23 non-null float64 4 Bay of Plenty 23 non-null float64 5 Gisborne 23 non-null float64 6 Hawke's Bay 23 non-null float64 7 Taranaki 23 non-null float64 8 Manawatu-Whanganui 23 non-null float64 9 Wellington 23 non-null float64 10 West Coast 23 non-null float64 11 Canterbury 23 non-null float64 12 Otago 23 non-null float64 13 Southland 23 non-null float64 14 Marlborough 23 non-null float64 15 Tasman/Nelson 23 non-null float64 16 Total North Island 23 non-null float64 17 Total South Island 23 non-null float64 18 New Zealand 23 non-null float64 dtypes: float64(18), int64(1) memory usage: 3.7 KB None
Plot trends to select data imputation method
GDP_region.set_index('Year', inplace=True)
plt.figure(figsize=(8, 4))
for column in GDP_region.columns:
plt.plot(GDP_region.index, GDP_region[column], marker='o', label=column)
plt.title('GDP Trends by Region')
plt.grid(False)
plt.show()
As can be seen from the graph above, GDP is rising in every region, suggesting that we can *use the growth rates of recent years to predict* the values for 2023.
# Calculate the growth rate for each region from 2021 to 2022
growth_rates = (GDP_region.loc[2022] - GDP_region.loc[2021]) / GDP_region.loc[2021]
# Projected data for 2023 based on data for 2022 and calculated growth rates
GDP_region.loc[2023] = GDP_region.loc[2022] * (1 + growth_rates)
print(GDP_region.loc[2023])
Northland 9663.231164 Auckland 136314.175126 Waikato 32019.992728 Bay of Plenty 21169.427548 Gisborne 2381.703901 Hawke's Bay 10758.225839 Taranaki 9831.115637 Manawatu-Whanganui 14186.718943 Wellington 44215.383955 West Coast 1812.109865 Canterbury 44055.669936 Otago 15275.914152 Southland 8258.371521 Marlborough 3371.313419 Tasman/Nelson 6531.251164 Total North Island 280519.405016 Total South Island 79253.753487 New Zealand 359772.985932 Name: 2023, dtype: float64
Finally, the data format is adjusted to preview the data.
GDP_region.reset_index(inplace=True)
# Data formatting adjustments
GDP_region.iloc[:, 1:] = GDP_region.iloc[:, 1:].round(1)
GDP_region
| Year | Northland | Auckland | Waikato | Bay of Plenty | Gisborne | Hawke's Bay | Taranaki | Manawatu-Whanganui | Wellington | West Coast | Canterbury | Otago | Southland | Marlborough | Tasman/Nelson | Total North Island | Total South Island | New Zealand | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2000 | 2655.0 | 37716.0 | 8582.0 | 5430.0 | 901.0 | 3386.0 | 3595.0 | 4742.0 | 16021.0 | 647.0 | 12215.0 | 4265.0 | 2355.0 | 935.0 | 1900.0 | 83028.0 | 22317.0 | 105345.0 |
| 1 | 2001 | 2954.0 | 38850.0 | 9712.0 | 5843.0 | 945.0 | 3643.0 | 4378.0 | 5065.0 | 16250.0 | 734.0 | 12849.0 | 4606.0 | 2746.0 | 1025.0 | 2035.0 | 87640.0 | 23995.0 | 111635.0 |
| 2 | 2002 | 3188.0 | 41840.0 | 10595.0 | 6238.0 | 986.0 | 3882.0 | 4479.0 | 5421.0 | 17424.0 | 773.0 | 13795.0 | 5030.0 | 2966.0 | 1135.0 | 2219.0 | 94054.0 | 25919.0 | 119973.0 |
| 3 | 2003 | 3082.0 | 45772.0 | 10181.0 | 6405.0 | 1008.0 | 4090.0 | 4344.0 | 5434.0 | 18184.0 | 744.0 | 14675.0 | 5347.0 | 2882.0 | 1160.0 | 2300.0 | 98500.0 | 27108.0 | 125607.0 |
| 4 | 2004 | 3316.0 | 49398.0 | 11038.0 | 6810.0 | 1020.0 | 4368.0 | 4327.0 | 5782.0 | 19021.0 | 818.0 | 15851.0 | 5732.0 | 2994.0 | 1276.0 | 2481.0 | 105079.0 | 29152.0 | 134231.0 |
| 5 | 2005 | 3659.0 | 53230.0 | 11666.0 | 7297.0 | 1073.0 | 4690.0 | 4356.0 | 6144.0 | 19957.0 | 862.0 | 17328.0 | 6198.0 | 2972.0 | 1351.0 | 2661.0 | 112073.0 | 31372.0 | 143444.0 |
| 6 | 2006 | 3969.0 | 55823.0 | 12516.0 | 7664.0 | 1087.0 | 4814.0 | 4518.0 | 6590.0 | 21190.0 | 1002.0 | 18272.0 | 6463.0 | 3163.0 | 1429.0 | 2653.0 | 118170.0 | 32982.0 | 151152.0 |
| 7 | 2007 | 4345.0 | 57384.0 | 13869.0 | 8399.0 | 1170.0 | 4946.0 | 5287.0 | 6695.0 | 21615.0 | 1075.0 | 19368.0 | 6807.0 | 3373.0 | 1584.0 | 2864.0 | 123709.0 | 35070.0 | 158779.0 |
| 8 | 2008 | 4619.0 | 61743.0 | 14708.0 | 9110.0 | 1211.0 | 4854.0 | 7649.0 | 7245.0 | 23220.0 | 1260.0 | 20901.0 | 7322.0 | 3873.0 | 1802.0 | 3047.0 | 134359.0 | 38204.0 | 172564.0 |
| 9 | 2009 | 4734.0 | 61098.0 | 15526.0 | 9039.0 | 1285.0 | 5061.0 | 7924.0 | 7165.0 | 23816.0 | 1388.0 | 21611.0 | 7577.0 | 4123.0 | 1851.0 | 3166.0 | 135648.0 | 39715.0 | 175363.0 |
| 10 | 2010 | 4543.0 | 63382.0 | 15258.0 | 9409.0 | 1324.0 | 5150.0 | 7665.0 | 7544.0 | 25113.0 | 1351.0 | 22229.0 | 7894.0 | 4168.0 | 1794.0 | 3296.0 | 139388.0 | 40731.0 | 180119.0 |
| 11 | 2011 | 4842.0 | 65759.0 | 16075.0 | 9947.0 | 1394.0 | 5447.0 | 7977.0 | 7888.0 | 25501.0 | 1427.0 | 23219.0 | 8171.0 | 4498.0 | 1813.0 | 3446.0 | 144830.0 | 42574.0 | 187404.0 |
| 12 | 2012 | 4922.0 | 68978.0 | 17102.0 | 10321.0 | 1432.0 | 5575.0 | 7896.0 | 7997.0 | 26724.0 | 1517.0 | 24316.0 | 8402.0 | 4599.0 | 1919.0 | 3503.0 | 150947.0 | 44256.0 | 195203.0 |
| 13 | 2013 | 4899.0 | 71351.0 | 16725.0 | 10490.0 | 1427.0 | 5730.0 | 7967.0 | 7999.0 | 27149.0 | 1449.0 | 25371.0 | 8526.0 | 4432.0 | 2001.0 | 3581.0 | 153739.0 | 45360.0 | 199099.0 |
| 14 | 2014 | 5363.0 | 75444.0 | 18690.0 | 11030.0 | 1474.0 | 6044.0 | 8508.0 | 8558.0 | 28348.0 | 1548.0 | 28198.0 | 9224.0 | 5075.0 | 2224.0 | 3873.0 | 163457.0 | 50143.0 | 213600.0 |
| 15 | 2015 | 5555.0 | 81076.0 | 18754.0 | 11291.0 | 1541.0 | 6176.0 | 8329.0 | 8681.0 | 29588.0 | 1479.0 | 29434.0 | 9427.0 | 4841.0 | 2338.0 | 3996.0 | 170990.0 | 51516.0 | 222506.0 |
| 16 | 2016 | 5994.0 | 88139.0 | 19413.0 | 12287.0 | 1638.0 | 6444.0 | 7221.0 | 8944.0 | 30654.0 | 1380.0 | 30338.0 | 10057.0 | 4693.0 | 2440.0 | 4216.0 | 180735.0 | 53124.0 | 233859.0 |
| 17 | 2017 | 6428.0 | 93624.0 | 20709.0 | 13820.0 | 1707.0 | 7011.0 | 7279.0 | 9358.0 | 32240.0 | 1450.0 | 31189.0 | 10745.0 | 5122.0 | 2604.0 | 4492.0 | 192176.0 | 55602.0 | 247778.0 |
| 18 | 2018 | 6970.0 | 99794.0 | 22583.0 | 15106.0 | 1844.0 | 7567.0 | 8129.0 | 10123.0 | 33610.0 | 1632.0 | 33555.0 | 11754.0 | 5609.0 | 2798.0 | 4835.0 | 205727.0 | 60184.0 | 265911.0 |
| 19 | 2019 | 7238.0 | 105433.0 | 24349.0 | 16322.0 | 1999.0 | 7886.0 | 8245.0 | 10744.0 | 34849.0 | 1685.0 | 35066.0 | 12484.0 | 5932.0 | 2966.0 | 5162.0 | 217065.0 | 63295.0 | 280360.0 |
| 20 | 2020 | 7712.0 | 110312.0 | 25993.0 | 17283.0 | 2056.0 | 8397.0 | 8483.0 | 11344.0 | 37561.0 | 1725.0 | 37099.0 | 13097.0 | 6093.0 | 3135.0 | 5487.0 | 229139.0 | 66638.0 | 295777.0 |
| 21 | 2021 | 7804.0 | 113290.0 | 26954.0 | 18012.0 | 2256.0 | 8878.0 | 8077.0 | 12115.0 | 37606.0 | 1784.0 | 37011.0 | 12860.0 | 6180.0 | 3197.0 | 5582.0 | 234991.0 | 66613.0 | 301604.0 |
| 22 | 2022 | 8684.0 | 124270.0 | 29378.0 | 19527.0 | 2318.0 | 9773.0 | 8911.0 | 13110.0 | 40777.0 | 1798.0 | 40380.0 | 14016.0 | 7144.0 | 3283.0 | 6038.0 | 256748.0 | 72659.0 | 329407.0 |
| 23 | 2023 | 9663.2 | 136314.2 | 32020.0 | 21169.4 | 2381.7 | 10758.2 | 9831.1 | 14186.7 | 44215.4 | 1812.1 | 44055.7 | 15275.9 | 8258.4 | 3371.3 | 6531.3 | 280519.4 | 79253.8 | 359773.0 |
1.2.2.2 GPD industryΒΆ
This dataset consists of New Zealand GDP data by industry for past years.
# import data
file_path = "../AAA/datasets/GDP_industry.csv"
GDP_industry = pd.read_csv(file_path)
GDP_industry.head()
| Year | Agriculture | Forestry, Fishing, and Mining | Forestry, Fishing, Mining, Electricity, Gas, Water and Waste Services | Primary Manufacturing | Other Manufacturing | Manufacturing | Electricity, Gas, Water, and Waste services | Construction | Wholesale Trade | Retail Trade | Accommodation and Food Services | Accommodation | Food and beverage services | Transport, Postal and Warehousing | Information Media, Telecommunications and Other Services | Financial and Insurance Services | Rental, Hiring and Real Estate Services | Owner-Occupied Property Operation | Professional, Scientific, and Technical Services | Administrative and Support Services | Public Administration and Safety | Education and Training | Health Care and Social Assistance | Total All Industries | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2000 | 5165 | 3138 | 6388 | 11156 | 6458 | 17614 | 3250 | 5179 | 6211 | 5086 | 2086.0 | 701 | 1386 | 5274 | 8460 | 4956 | 6355 | 8416 | 7594 | 2064 | 4313 | 4838 | 5344 | 105345 |
| 1 | 2001 | 7124 | 3549 | 6718 | 12070 | 6510 | 18580 | 3169 | 5139 | 6621 | 5346 | 2317.0 | 810 | 1506 | 5297 | 8924 | 5390 | 6711 | 8522 | 7596 | 2067 | 4505 | 5066 | 5711 | 111635 |
| 2 | 2002 | 8048 | 3648 | 6935 | 12513 | 7038 | 19551 | 3287 | 5484 | 7433 | 6130 | 2521.0 | 891 | 1630 | 5612 | 9539 | 5906 | 7187 | 8707 | 8426 | 2321 | 4794 | 5207 | 6171 | 119973 |
| 3 | 2003 | 5750 | 3639 | 7337 | 13618 | 7405 | 21023 | 3698 | 6053 | 7259 | 6826 | 2477.0 | 851 | 1626 | 6129 | 10420 | 6430 | 7856 | 9221 | 9095 | 2553 | 5039 | 5553 | 6586 | 125607 |
| 4 | 2004 | 6251 | 3352 | 7562 | 13378 | 7890 | 21268 | 4210 | 6806 | 7932 | 7202 | 2832.0 | 978 | 1854 | 6586 | 11070 | 7289 | 8641 | 9840 | 9672 | 2733 | 5310 | 6044 | 7195 | 134231 |
GDP_industry.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 23 entries, 0 to 22 Data columns (total 25 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Year 23 non-null int64 1 Agriculture 23 non-null int64 2 Forestry, Fishing, and Mining 23 non-null int64 3 Forestry, Fishing, Mining, Electricity, Gas, Water and Waste Services 23 non-null int64 4 Primary Manufacturing 23 non-null int64 5 Other Manufacturing 23 non-null int64 6 Manufacturing 23 non-null int64 7 Electricity, Gas, Water, and Waste services 23 non-null int64 8 Construction 23 non-null int64 9 Wholesale Trade 23 non-null int64 10 Retail Trade 23 non-null int64 11 Accommodation and Food Services 16 non-null float64 12 Accommodation 23 non-null int64 13 Food and beverage services 23 non-null int64 14 Transport, Postal and Warehousing 23 non-null int64 15 Information Media, Telecommunications and Other Services 23 non-null int64 16 Financial and Insurance Services 23 non-null int64 17 Rental, Hiring and Real Estate Services 23 non-null int64 18 Owner-Occupied Property Operation 23 non-null int64 19 Professional, Scientific, and Technical Services 23 non-null int64 20 Administrative and Support Services 23 non-null int64 21 Public Administration and Safety 23 non-null int64 22 Education and Training 23 non-null int64 23 Health Care and Social Assistance 23 non-null int64 24 Total All Industries 23 non-null int64 dtypes: float64(1), int64(24) memory usage: 4.6 KB
Examining the data shows that there are *null values in the variable 'Accommodation and Food Services'* and further examination of all values in this variable is required.
print(GDP_industry['Accommodation and Food Services'])
0 2086.0 1 2317.0 2 2521.0 3 2477.0 4 2832.0 5 3148.0 6 3216.0 7 3365.0 8 3649.0 9 3505.0 10 3789.0 11 3857.0 12 3940.0 13 4124.0 14 4368.0 15 4763.0 16 NaN 17 NaN 18 NaN 19 NaN 20 NaN 21 NaN 22 NaN Name: Accommodation and Food Services, dtype: float64
To complete imputing this variable the following strategy is used
- Identify *the variable most correlated with 'accommodation and food services'*.
- Use the data from the variable with the highest correlation to run a *regression*.
- *Apply regression analysis to forecast the GDP* of the 'Accommodation and Food Services' industry
# Calculate the correlation coefficient matrix
corr_matrix = GDP_industry.drop('Year', axis=1).corr()
# Plot heat map
plt.figure(figsize=(8, 6))
sns.heatmap(corr_matrix, annot=False, cmap='coolwarm', cbar=True)
plt.show()
From the heat map it can be seen that the variable with the highest correlation with the variable 'accommodation and food services' is 'food and beverage services'. Therefore, *'Food and Beverage Services' is used for the regression prediction.*
from sklearn.linear_model import LinearRegression
# Split data into parts with and without missing values
train_data = GDP_industry.dropna(subset=['Accommodation and Food Services'])
test_data = GDP_industry[GDP_industry['Accommodation and Food Services'].isna()]
# Define the independent and dependent variables
X_train = train_data[['Food and beverage services']]
y_train = train_data['Accommodation and Food Services']
X_test = test_data[['Food and beverage services']]
# Build a linear regression model
model = LinearRegression()
model.fit(X_train, y_train)
# Predicted missing values
predicted_values = model.predict(X_test)
# Impute missing value
GDP_industry.loc[GDP_industry['Accommodation and Food Services'].isna(), 'Accommodation and Food Services'] = predicted_values
# Display of final data
GDP_industry['Accommodation and Food Services'] = GDP_industry['Accommodation and Food Services'].astype('int64')
GDP_industry['Accommodation and Food Services']
0 2086 1 2317 2 2521 3 2477 4 2832 5 3148 6 3216 7 3365 8 3649 9 3505 10 3789 11 3857 12 3940 13 4124 14 4368 15 4763 16 5327 17 5947 18 6247 19 6471 20 6823 21 6002 22 6851 Name: Accommodation and Food Services, dtype: int64
For the GDP dataset, the pre-processing steps have been completed for
- GDP by region
- GDP by industry
2.Data IntegrationΒΆ
For further analysis, we need to merge the static datasets from the data pre-processing stage into one *master table. Including: 1.Job market*
- Online job
- Regional employment
- Labour force status
- Median wagee:
2.GDP
- GDP by region
- GDP by industry
To complete the data integration, the following steps were taken:
- Preparation: The structure and naming of each table are standardised to ensure consistency across data sources.
- Merge: A custom function was implemented to merge any number of datasets with a common key efficiently. This allows us to input all datasets simultaneously and merge them quickly.
- Big data processing optimisation: Using Dask DataFrame and HDF5 formats. This phase also included creating a custom function to quickly rename columns.
2.1 Job market datasetsΒΆ
2.1.1 Online job datasetsΒΆ
The dataset is collected quarterly and must be converted to annual data for merging.
# Convert a date string to a date object
df_jobonline_all['Date'] = pd.to_datetime(df_jobonline_all['Date'])
# Calculate quarters and format dates
df_jobonline_all['Date'] = df_jobonline_all['Date'].dt.year.astype(str) + 'Q' + ((df_jobonline_all['Date'].dt.month - 1) // 3 + 1).astype(str)
df_jobonline_all['Year'] = df_jobonline_all['Date'].str[:4].astype(int)
numeric_cols = df_jobonline_all.select_dtypes(include=[np.number]).columns.tolist()
if 'Year' in numeric_cols:
numeric_cols.remove('Year')
# Grouping by year and averaging the numerical columns
job_online = df_jobonline_all.groupby('Year',as_index=True)[numeric_cols].mean()
job_online.reset_index(drop=False, inplace=True)
job_online.head()
| Year | Auckland | Bay of Plenty | Canterbury | Gisborne Hawke's Bay | Marlborough/NelsonTasman/West Coast | Manawatu/Wanganui-Taranaki | Northland | Otago Southland | Waikato | Wellington | ids_Accounting | ids_Construction | ids_Education | ids_Health | ids_Hospitalty | ids_IT | ids_Manufacturing | ids_Primary | ids_Sales | ids_Other | ocp_Managers | ocp_Professionals | ocp_Tech & Trades | ocp_Community & Personal Services | ocp_Clerical & Administration | ocp_Sales | ocp_Machinery Drivers | ocp_Labourers | ocp_highly skilled | ocp_skilled | ocp_semi-skilled | ocp_low skilled | ocp_unskilled | ocp_Skilled | ocp_Unskilled | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2010 | 100.00000 | 100.00000 | 100.00000 | 100.00000 | 100.00000 | 100.00000 | 100.0000 | 100.00000 | 100.00000 | 100.00000 | 100.00000 | 100.00000 | 100.00000 | 100.00000 | 100.00000 | 100.00000 | 100.00000 | 100.00000 | 100.00000 | 100.00000 | 100.00000 | 100.00000 | 100.00000 | 100.00000 | 100.00000 | 100.00000 | 100.0000 | 100.00000 | 100.00000 | 100.00000 | 100.00000 | 100.00000 | 100.00000 | 100.00000 | 100.00000 |
| 1 | 2011 | 101.53725 | 103.78475 | 131.81150 | 107.87775 | 111.24925 | 104.01875 | 109.5020 | 116.04450 | 105.59650 | 101.32300 | 106.24775 | 115.75200 | 92.19875 | 104.26875 | 107.14425 | 102.21425 | 111.41325 | 123.85425 | 101.98475 | 108.94725 | 103.91725 | 106.69575 | 121.15200 | 115.34075 | 107.32600 | 105.28125 | 113.4085 | 118.31375 | 105.89775 | 103.69375 | 126.95700 | 111.03375 | 104.36725 | 108.66475 | 109.75675 |
| 2 | 2012 | 104.07175 | 110.63300 | 170.32750 | 120.65275 | 129.16900 | 117.80575 | 115.1725 | 138.57875 | 110.88150 | 102.74725 | 109.49900 | 147.46800 | 83.28000 | 119.27800 | 112.45725 | 97.90175 | 125.17300 | 148.83975 | 105.11225 | 113.70325 | 106.71575 | 107.81950 | 142.38200 | 133.33875 | 113.67750 | 107.55275 | 137.1715 | 145.01325 | 107.70150 | 110.10950 | 150.74725 | 120.93250 | 116.90500 | 113.88775 | 120.46675 |
| 3 | 2013 | 116.27650 | 130.56400 | 191.61625 | 127.15625 | 136.18075 | 137.26350 | 120.3040 | 150.94075 | 123.46825 | 106.19475 | 112.27775 | 184.80025 | 92.26350 | 123.59450 | 134.52225 | 92.37850 | 153.48125 | 158.91900 | 113.93700 | 122.01075 | 116.59175 | 108.56500 | 171.24675 | 157.54900 | 120.54600 | 118.16900 | 180.6850 | 190.80675 | 110.85300 | 123.29000 | 181.36625 | 137.46225 | 138.64800 | 123.33150 | 138.68850 |
| 4 | 2014 | 133.69175 | 154.13400 | 220.14950 | 143.16750 | 165.08475 | 144.57075 | 141.1915 | 188.93475 | 140.00225 | 111.70625 | 121.78200 | 230.17750 | 106.67700 | 129.58350 | 162.81525 | 95.91100 | 181.68300 | 183.42750 | 128.40525 | 121.79475 | 135.62400 | 115.74175 | 207.52175 | 180.00800 | 132.23875 | 131.47725 | 223.2990 | 249.28800 | 120.81950 | 142.89900 | 209.12425 | 158.86300 | 167.51250 | 138.88250 | 162.42250 |
Change column names to make it easier to identify variables in the merged table.
# Get all the columns except βYearβ
columns_to_rename = [col for col in job_online.columns if col != 'Year']
# Construct a dictionary that maps old column names to new column names
rename_dict = {col: 'AVI_' + col for col in columns_to_rename}
# Use the rename() method to rename a column
job_online =job_online.rename(columns=rename_dict)
job_online.head()
| Year | AVI_Auckland | AVI_Bay of Plenty | AVI_Canterbury | AVI_Gisborne Hawke's Bay | AVI_Marlborough/NelsonTasman/West Coast | AVI_Manawatu/Wanganui-Taranaki | AVI_Northland | AVI_Otago Southland | AVI_Waikato | AVI_Wellington | AVI_ids_Accounting | AVI_ids_Construction | AVI_ids_Education | AVI_ids_Health | AVI_ids_Hospitalty | AVI_ids_IT | AVI_ids_Manufacturing | AVI_ids_Primary | AVI_ids_Sales | AVI_ids_Other | AVI_ocp_Managers | AVI_ocp_Professionals | AVI_ocp_Tech & Trades | AVI_ocp_Community & Personal Services | AVI_ocp_Clerical & Administration | AVI_ocp_Sales | AVI_ocp_Machinery Drivers | AVI_ocp_Labourers | AVI_ocp_highly skilled | AVI_ocp_skilled | AVI_ocp_semi-skilled | AVI_ocp_low skilled | AVI_ocp_unskilled | AVI_ocp_Skilled | AVI_ocp_Unskilled | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2010 | 100.00000 | 100.00000 | 100.00000 | 100.00000 | 100.00000 | 100.00000 | 100.0000 | 100.00000 | 100.00000 | 100.00000 | 100.00000 | 100.00000 | 100.00000 | 100.00000 | 100.00000 | 100.00000 | 100.00000 | 100.00000 | 100.00000 | 100.00000 | 100.00000 | 100.00000 | 100.00000 | 100.00000 | 100.00000 | 100.00000 | 100.0000 | 100.00000 | 100.00000 | 100.00000 | 100.00000 | 100.00000 | 100.00000 | 100.00000 | 100.00000 |
| 1 | 2011 | 101.53725 | 103.78475 | 131.81150 | 107.87775 | 111.24925 | 104.01875 | 109.5020 | 116.04450 | 105.59650 | 101.32300 | 106.24775 | 115.75200 | 92.19875 | 104.26875 | 107.14425 | 102.21425 | 111.41325 | 123.85425 | 101.98475 | 108.94725 | 103.91725 | 106.69575 | 121.15200 | 115.34075 | 107.32600 | 105.28125 | 113.4085 | 118.31375 | 105.89775 | 103.69375 | 126.95700 | 111.03375 | 104.36725 | 108.66475 | 109.75675 |
| 2 | 2012 | 104.07175 | 110.63300 | 170.32750 | 120.65275 | 129.16900 | 117.80575 | 115.1725 | 138.57875 | 110.88150 | 102.74725 | 109.49900 | 147.46800 | 83.28000 | 119.27800 | 112.45725 | 97.90175 | 125.17300 | 148.83975 | 105.11225 | 113.70325 | 106.71575 | 107.81950 | 142.38200 | 133.33875 | 113.67750 | 107.55275 | 137.1715 | 145.01325 | 107.70150 | 110.10950 | 150.74725 | 120.93250 | 116.90500 | 113.88775 | 120.46675 |
| 3 | 2013 | 116.27650 | 130.56400 | 191.61625 | 127.15625 | 136.18075 | 137.26350 | 120.3040 | 150.94075 | 123.46825 | 106.19475 | 112.27775 | 184.80025 | 92.26350 | 123.59450 | 134.52225 | 92.37850 | 153.48125 | 158.91900 | 113.93700 | 122.01075 | 116.59175 | 108.56500 | 171.24675 | 157.54900 | 120.54600 | 118.16900 | 180.6850 | 190.80675 | 110.85300 | 123.29000 | 181.36625 | 137.46225 | 138.64800 | 123.33150 | 138.68850 |
| 4 | 2014 | 133.69175 | 154.13400 | 220.14950 | 143.16750 | 165.08475 | 144.57075 | 141.1915 | 188.93475 | 140.00225 | 111.70625 | 121.78200 | 230.17750 | 106.67700 | 129.58350 | 162.81525 | 95.91100 | 181.68300 | 183.42750 | 128.40525 | 121.79475 | 135.62400 | 115.74175 | 207.52175 | 180.00800 | 132.23875 | 131.47725 | 223.2990 | 249.28800 | 120.81950 | 142.89900 | 209.12425 | 158.86300 | 167.51250 | 138.88250 | 162.42250 |
2.1.2 Region dataΒΆ
# Add prefixes and rename variables
job_creation.reset_index(drop=False, inplace=True)
# Get all the columns except βYearβ
columns_to_rename = [col for col in job_creation.columns if col != 'Year']
# Construct a dictionary that maps old column names to new column names
rename_dict = {col: 'job_creation_' + col for col in columns_to_rename}
job_creation = job_creation.rename(columns=rename_dict)
job_creation.head()
| Year | job_creation_Auckland | job_creation_Bay of Plenty | job_creation_Canterbury | job_creation_Gisborne | job_creation_Hawke's Bay | job_creation_Manawatu-Wanganui | job_creation_Otago | job_creation_Taranaki | job_creation_Tasman, Nelson, Marlborough, West Coast | job_creation_Waikato | job_creation_Wellington | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1999 | 5750 | 630 | 1490 | 65 | 270 | 390 | 390 | 210 | 230 | 840 | 2180 |
| 1 | 2000 | 13440 | 1420 | 3290 | 150 | 880 | 970 | 980 | 630 | 540 | 2210 | 6050 |
| 2 | 2001 | 15100 | 1350 | 4580 | 165 | 810 | 850 | 1000 | 600 | 590 | 2190 | 6820 |
| 3 | 2002 | 14720 | 1680 | 2940 | 190 | 1170 | 850 | 840 | 620 | 620 | 2080 | 5810 |
| 4 | 2003 | 14590 | 1940 | 3270 | 150 | 820 | 1040 | 1140 | 710 | 710 | 2220 | 4830 |
# Add prefixes and rename variables
job_destruction.reset_index(drop=False, inplace=True)
columns_to_rename = [col for col in job_destruction.columns if col != 'Year']
rename_dict = {col: 'job_destruction_' + col for col in columns_to_rename}
job_destruction = job_destruction.rename(columns=rename_dict)
job_destruction.head()
| Year | job_destruction_Auckland | job_destruction_Bay of Plenty | job_destruction_Canterbury | job_destruction_Gisborne | job_destruction_Hawke's Bay | job_destruction_Manawatu-Wanganui | job_destruction_Otago | job_destruction_Taranaki | job_destruction_Tasman, Nelson, Marlborough, West Coast | job_destruction_Waikato | job_destruction_Wellington | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1999 | 4510 | 430 | 990 | 60 | 275 | 300 | 570 | 145 | 165 | 720 | 2130 |
| 1 | 2000 | 13200 | 1130 | 3040 | 145 | 750 | 930 | 1070 | 540 | 510 | 1800 | 5350 |
| 2 | 2001 | 12410 | 940 | 4010 | 120 | 730 | 980 | 870 | 450 | 510 | 1460 | 6180 |
| 3 | 2002 | 12760 | 1660 | 2960 | 165 | 890 | 710 | 790 | 480 | 500 | 1640 | 5970 |
| 4 | 2003 | 12310 | 1680 | 2480 | 145 | 880 | 770 | 740 | 430 | 530 | 1420 | 5010 |
job_creation['Year'] = job_creation['Year'].astype(int)
job_destruction['Year'] = job_destruction['Year'].astype(int)
2.1.3 Median WageΒΆ
# Add prefixes and rename variables
columns_to_rename = [col for col in age_median_wage_year.columns if col != 'Year']
rename_dict = {col: 'median_wage_' + col for col in columns_to_rename}
age_median_wage_year = age_median_wage_year.rename(columns=rename_dict)
age_median_wage_year.head()
| Year | median_wage_15-19 | median_wage_20-24 | median_wage_25-29 | median_wage_30-34 | median_wage_35-39 | median_wage_40-44 | median_wage_45-49 | median_wage_50-54 | median_wage_55-59 | median_wage_60-64 | median_wage_65 | median_wage_Total All Ages | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1999 | 1783.3 | 5946.7 | 7896.7 | 8470.0 | 8356.7 | 8210.0 | 8306.7 | 8146.7 | 7703.3 | 6926.7 | 2610.0 | 7396.7 |
| 1 | 2000 | 2017.5 | 6042.5 | 7960.0 | 8575.0 | 8477.5 | 8317.5 | 8390.0 | 8230.0 | 7757.5 | 7035.0 | 2845.0 | 7512.5 |
| 2 | 2001 | 2190.0 | 6190.0 | 8165.0 | 8825.0 | 8755.0 | 8602.5 | 8650.0 | 8507.5 | 8032.5 | 7262.5 | 3282.5 | 7745.0 |
| 3 | 2002 | 2355.0 | 6370.0 | 8392.5 | 9107.5 | 9067.5 | 8947.5 | 8945.0 | 8837.5 | 8385.0 | 7567.5 | 3675.0 | 8002.5 |
| 4 | 2003 | 2505.0 | 6522.5 | 8620.0 | 9397.5 | 9372.5 | 9270.0 | 9275.0 | 9167.5 | 8727.5 | 7900.0 | 4090.0 | 8260.0 |
2.2 GDP datasetsΒΆ
2.2.1 GDP regionΒΆ
# Add prefixes and rename variables
columns_to_rename = [col for col in GDP_region.columns if col != 'Year']
rename_dict = {col: 'GDP_' + col for col in columns_to_rename}
GDP_region = GDP_region.rename(columns=rename_dict)
GDP_region.head()
| Year | GDP_Northland | GDP_Auckland | GDP_Waikato | GDP_Bay of Plenty | GDP_Gisborne | GDP_Hawke's Bay | GDP_Taranaki | GDP_Manawatu-Whanganui | GDP_Wellington | GDP_West Coast | GDP_Canterbury | GDP_Otago | GDP_Southland | GDP_Marlborough | GDP_Tasman/Nelson | GDP_Total North Island | GDP_Total South Island | GDP_New Zealand | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2000 | 2655.0 | 37716.0 | 8582.0 | 5430.0 | 901.0 | 3386.0 | 3595.0 | 4742.0 | 16021.0 | 647.0 | 12215.0 | 4265.0 | 2355.0 | 935.0 | 1900.0 | 83028.0 | 22317.0 | 105345.0 |
| 1 | 2001 | 2954.0 | 38850.0 | 9712.0 | 5843.0 | 945.0 | 3643.0 | 4378.0 | 5065.0 | 16250.0 | 734.0 | 12849.0 | 4606.0 | 2746.0 | 1025.0 | 2035.0 | 87640.0 | 23995.0 | 111635.0 |
| 2 | 2002 | 3188.0 | 41840.0 | 10595.0 | 6238.0 | 986.0 | 3882.0 | 4479.0 | 5421.0 | 17424.0 | 773.0 | 13795.0 | 5030.0 | 2966.0 | 1135.0 | 2219.0 | 94054.0 | 25919.0 | 119973.0 |
| 3 | 2003 | 3082.0 | 45772.0 | 10181.0 | 6405.0 | 1008.0 | 4090.0 | 4344.0 | 5434.0 | 18184.0 | 744.0 | 14675.0 | 5347.0 | 2882.0 | 1160.0 | 2300.0 | 98500.0 | 27108.0 | 125607.0 |
| 4 | 2004 | 3316.0 | 49398.0 | 11038.0 | 6810.0 | 1020.0 | 4368.0 | 4327.0 | 5782.0 | 19021.0 | 818.0 | 15851.0 | 5732.0 | 2994.0 | 1276.0 | 2481.0 | 105079.0 | 29152.0 | 134231.0 |
2.2.2 GDP industryΒΆ
# Add prefixes and rename variables
columns_to_rename = [col for col in GDP_industry.columns if col != 'Year']
rename_dict = {col: 'GDP_' + col for col in columns_to_rename}
GDP_industry = GDP_industry.rename(columns=rename_dict)
GDP_industry.head()
| Year | GDP_Agriculture | GDP_Forestry, Fishing, and Mining | GDP_Forestry, Fishing, Mining, Electricity, Gas, Water and Waste Services | GDP_Primary Manufacturing | GDP_Other Manufacturing | GDP_Manufacturing | GDP_Electricity, Gas, Water, and Waste services | GDP_Construction | GDP_Wholesale Trade | GDP_Retail Trade | GDP_Accommodation and Food Services | GDP_Accommodation | GDP_Food and beverage services | GDP_Transport, Postal and Warehousing | GDP_Information Media, Telecommunications and Other Services | GDP_Financial and Insurance Services | GDP_Rental, Hiring and Real Estate Services | GDP_Owner-Occupied Property Operation | GDP_Professional, Scientific, and Technical Services | GDP_Administrative and Support Services | GDP_Public Administration and Safety | GDP_Education and Training | GDP_Health Care and Social Assistance | GDP_Total All Industries | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2000 | 5165 | 3138 | 6388 | 11156 | 6458 | 17614 | 3250 | 5179 | 6211 | 5086 | 2086 | 701 | 1386 | 5274 | 8460 | 4956 | 6355 | 8416 | 7594 | 2064 | 4313 | 4838 | 5344 | 105345 |
| 1 | 2001 | 7124 | 3549 | 6718 | 12070 | 6510 | 18580 | 3169 | 5139 | 6621 | 5346 | 2317 | 810 | 1506 | 5297 | 8924 | 5390 | 6711 | 8522 | 7596 | 2067 | 4505 | 5066 | 5711 | 111635 |
| 2 | 2002 | 8048 | 3648 | 6935 | 12513 | 7038 | 19551 | 3287 | 5484 | 7433 | 6130 | 2521 | 891 | 1630 | 5612 | 9539 | 5906 | 7187 | 8707 | 8426 | 2321 | 4794 | 5207 | 6171 | 119973 |
| 3 | 2003 | 5750 | 3639 | 7337 | 13618 | 7405 | 21023 | 3698 | 6053 | 7259 | 6826 | 2477 | 851 | 1626 | 6129 | 10420 | 6430 | 7856 | 9221 | 9095 | 2553 | 5039 | 5553 | 6586 | 125607 |
| 4 | 2004 | 6251 | 3352 | 7562 | 13378 | 7890 | 21268 | 4210 | 6806 | 7932 | 7202 | 2832 | 978 | 1854 | 6586 | 11070 | 7289 | 8641 | 9840 | 9672 | 2733 | 5310 | 6044 | 7195 | 134231 |
2.3 Merge datasetsΒΆ
Define a function to merge different datasets.
The merge_datasets function is designed to *merge multiple datasets based on a common key, in this case the 'Year' column. It takes any number of datasets as input* and merges them iteratively using an inner join operation on the 'Year' column. It *returns the merged dataset*. This function provides a convenient way of efficiently merging datasets with a common key.
def merge_datasets(*datasets):
merged_dataset = datasets[0] # Initialize with the first dataset
# Iterate over the remaining datasets and merge them one by one
for dataset in datasets[1:]:
merged_dataset = pd.merge(merged_dataset, dataset, on='Year', how='inner')
return merged_dataset
Merge all processed datasets at once using a custom function.
merged_data = merge_datasets(job_creation, job_destruction, labour_force_status, job_online,age_median_wage_year,GDP_region, GDP_industry)
merged_data
| Year | job_creation_Auckland | job_creation_Bay of Plenty | job_creation_Canterbury | job_creation_Gisborne | job_creation_Hawke's Bay | job_creation_Manawatu-Wanganui | job_creation_Otago | job_creation_Taranaki | job_creation_Tasman, Nelson, Marlborough, West Coast | job_creation_Waikato | job_creation_Wellington | job_destruction_Auckland | job_destruction_Bay of Plenty | job_destruction_Canterbury | job_destruction_Gisborne | job_destruction_Hawke's Bay | job_destruction_Manawatu-Wanganui | job_destruction_Otago | job_destruction_Taranaki | job_destruction_Tasman, Nelson, Marlborough, West Coast | job_destruction_Waikato | job_destruction_Wellington | Not in Labour Force | Working Age Population | Labour Force Participation Rate | Unemployment Rate | Employment Rate | Total Labour Force | AVI_Auckland | AVI_Bay of Plenty | AVI_Canterbury | AVI_Gisborne Hawke's Bay | AVI_Marlborough/NelsonTasman/West Coast | AVI_Manawatu/Wanganui-Taranaki | AVI_Northland | AVI_Otago Southland | AVI_Waikato | AVI_Wellington | AVI_ids_Accounting | AVI_ids_Construction | AVI_ids_Education | AVI_ids_Health | AVI_ids_Hospitalty | AVI_ids_IT | AVI_ids_Manufacturing | AVI_ids_Primary | AVI_ids_Sales | AVI_ids_Other | AVI_ocp_Managers | AVI_ocp_Professionals | AVI_ocp_Tech & Trades | AVI_ocp_Community & Personal Services | AVI_ocp_Clerical & Administration | AVI_ocp_Sales | AVI_ocp_Machinery Drivers | AVI_ocp_Labourers | AVI_ocp_highly skilled | AVI_ocp_skilled | AVI_ocp_semi-skilled | AVI_ocp_low skilled | AVI_ocp_unskilled | AVI_ocp_Skilled | AVI_ocp_Unskilled | median_wage_15-19 | median_wage_20-24 | median_wage_25-29 | median_wage_30-34 | median_wage_35-39 | median_wage_40-44 | median_wage_45-49 | median_wage_50-54 | median_wage_55-59 | median_wage_60-64 | median_wage_65 | median_wage_Total All Ages | GDP_Northland | GDP_Auckland | GDP_Waikato | GDP_Bay of Plenty | GDP_Gisborne | GDP_Hawke's Bay | GDP_Taranaki | GDP_Manawatu-Whanganui | GDP_Wellington | GDP_West Coast | GDP_Canterbury | GDP_Otago | GDP_Southland | GDP_Marlborough | GDP_Tasman/Nelson | GDP_Total North Island | GDP_Total South Island | GDP_New Zealand | GDP_Agriculture | GDP_Forestry, Fishing, and Mining | GDP_Forestry, Fishing, Mining, Electricity, Gas, Water and Waste Services | GDP_Primary Manufacturing | GDP_Other Manufacturing | GDP_Manufacturing | GDP_Electricity, Gas, Water, and Waste services | GDP_Construction | GDP_Wholesale Trade | GDP_Retail Trade | GDP_Accommodation and Food Services | GDP_Accommodation | GDP_Food and beverage services | GDP_Transport, Postal and Warehousing | GDP_Information Media, Telecommunications and Other Services | GDP_Financial and Insurance Services | GDP_Rental, Hiring and Real Estate Services | GDP_Owner-Occupied Property Operation | GDP_Professional, Scientific, and Technical Services | GDP_Administrative and Support Services | GDP_Public Administration and Safety | GDP_Education and Training | GDP_Health Care and Social Assistance | GDP_Total All Industries | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2010 | 18130 | 1580 | 3320 | 250 | 830 | 840 | 1240 | 475 | 800 | 2730 | 4910 | 15380 | 1430 | 3470 | 195 | 850 | 980 | 1300 | 515 | 900 | 2270 | 5290 | 645.7 | 2844.4 | 77.3 | 6.3 | 72.4 | 2198.7 | 100.00000 | 100.00000 | 100.00000 | 100.00000 | 100.00000 | 100.00000 | 100.00000 | 100.00000 | 100.00000 | 100.00000 | 100.00000 | 100.00000 | 100.00000 | 100.00000 | 100.00000 | 100.00000 | 100.00000 | 100.00000 | 100.00000 | 100.00000 | 100.00000 | 100.00000 | 100.00000 | 100.00000 | 100.00000 | 100.00000 | 100.00000 | 100.00000 | 100.00000 | 100.00000 | 100.00000 | 100.00000 | 100.00000 | 100.00000 | 100.00000 | 3060.0 | 8050.0 | 10582.5 | 11865.0 | 12315.0 | 12137.5 | 12065.0 | 11905.0 | 11437.5 | 10515.0 | 7245.0 | 10632.5 | 4543.0 | 63382.0 | 15258.0 | 9409.0 | 1324.0 | 5150.0 | 7665.0 | 7544.0 | 25113.0 | 1351.0 | 22229.0 | 7894.0 | 4168.0 | 1794.0 | 3296.0 | 139388.0 | 40731.0 | 180119.0 | 8654 | 6651 | 12720 | 13001 | 7925 | 20926 | 6069 | 10464 | 9869 | 8292 | 3789 | 1226 | 2563 | 7929 | 12490 | 11119 | 13413 | 12192 | 14735 | 3633 | 8732 | 9204 | 11958 | 180119 |
| 1 | 2011 | 17310 | 1600 | 4100 | 190 | 710 | 810 | 1140 | 480 | 850 | 2220 | 5310 | 14540 | 1370 | 3730 | 190 | 690 | 890 | 1110 | 490 | 760 | 1920 | 5120 | 655.8 | 2866.7 | 77.1 | 6.4 | 72.2 | 2211.0 | 101.53725 | 103.78475 | 131.81150 | 107.87775 | 111.24925 | 104.01875 | 109.50200 | 116.04450 | 105.59650 | 101.32300 | 106.24775 | 115.75200 | 92.19875 | 104.26875 | 107.14425 | 102.21425 | 111.41325 | 123.85425 | 101.98475 | 108.94725 | 103.91725 | 106.69575 | 121.15200 | 115.34075 | 107.32600 | 105.28125 | 113.40850 | 118.31375 | 105.89775 | 103.69375 | 126.95700 | 111.03375 | 104.36725 | 108.66475 | 109.75675 | 3152.5 | 8162.5 | 10835.0 | 12155.0 | 12712.5 | 12565.0 | 12462.5 | 12290.0 | 11845.0 | 10967.5 | 7752.5 | 10972.5 | 4842.0 | 65759.0 | 16075.0 | 9947.0 | 1394.0 | 5447.0 | 7977.0 | 7888.0 | 25501.0 | 1427.0 | 23219.0 | 8171.0 | 4498.0 | 1813.0 | 3446.0 | 144830.0 | 42574.0 | 187404.0 | 10643 | 6938 | 13291 | 13860 | 8191 | 22051 | 6353 | 10470 | 10081 | 8469 | 3857 | 1248 | 2609 | 8758 | 12421 | 9975 | 13648 | 13484 | 15529 | 3959 | 8835 | 9595 | 12337 | 187404 |
| 2 | 2012 | 17260 | 1560 | 4580 | 195 | 820 | 1040 | 1230 | 510 | 800 | 2710 | 5880 | 14410 | 1510 | 3710 | 160 | 920 | 940 | 1200 | 450 | 790 | 2290 | 5080 | 652.5 | 2876.3 | 77.3 | 6.3 | 72.4 | 2223.8 | 104.07175 | 110.63300 | 170.32750 | 120.65275 | 129.16900 | 117.80575 | 115.17250 | 138.57875 | 110.88150 | 102.74725 | 109.49900 | 147.46800 | 83.28000 | 119.27800 | 112.45725 | 97.90175 | 125.17300 | 148.83975 | 105.11225 | 113.70325 | 106.71575 | 107.81950 | 142.38200 | 133.33875 | 113.67750 | 107.55275 | 137.17150 | 145.01325 | 107.70150 | 110.10950 | 150.74725 | 120.93250 | 116.90500 | 113.88775 | 120.46675 | 3257.5 | 8370.0 | 11112.5 | 12452.5 | 13102.5 | 13020.0 | 12925.0 | 12735.0 | 12280.0 | 11435.0 | 8245.0 | 11335.0 | 4922.0 | 68978.0 | 17102.0 | 10321.0 | 1432.0 | 5575.0 | 7896.0 | 7997.0 | 26724.0 | 1517.0 | 24316.0 | 8402.0 | 4599.0 | 1919.0 | 3503.0 | 150947.0 | 44256.0 | 195203.0 | 10622 | 6772 | 13371 | 15298 | 8017 | 23315 | 6600 | 10804 | 10526 | 8878 | 3940 | 1245 | 2696 | 9331 | 12960 | 10625 | 14757 | 14344 | 15869 | 4172 | 9041 | 9985 | 12665 | 195203 |
| 3 | 2013 | 18320 | 1800 | 4360 | 305 | 790 | 770 | 1290 | 660 | 830 | 2110 | 5820 | 13340 | 1470 | 3770 | 245 | 710 | 780 | 1070 | 480 | 760 | 1950 | 4720 | 666.9 | 2878.8 | 76.8 | 6.6 | 71.8 | 2211.9 | 116.27650 | 130.56400 | 191.61625 | 127.15625 | 136.18075 | 137.26350 | 120.30400 | 150.94075 | 123.46825 | 106.19475 | 112.27775 | 184.80025 | 92.26350 | 123.59450 | 134.52225 | 92.37850 | 153.48125 | 158.91900 | 113.93700 | 122.01075 | 116.59175 | 108.56500 | 171.24675 | 157.54900 | 120.54600 | 118.16900 | 180.68500 | 190.80675 | 110.85300 | 123.29000 | 181.36625 | 137.46225 | 138.64800 | 123.33150 | 138.68850 | 3290.0 | 8555.0 | 11340.0 | 12705.0 | 13407.5 | 13420.0 | 13267.5 | 13065.0 | 12647.5 | 11850.0 | 8620.0 | 11617.5 | 4899.0 | 71351.0 | 16725.0 | 10490.0 | 1427.0 | 5730.0 | 7967.0 | 7999.0 | 27149.0 | 1449.0 | 25371.0 | 8526.0 | 4432.0 | 2001.0 | 3581.0 | 153739.0 | 45360.0 | 199099.0 | 9130 | 6610 | 13764 | 15133 | 7938 | 23071 | 7154 | 11541 | 10530 | 9489 | 4124 | 1270 | 2855 | 9790 | 13188 | 10921 | 15134 | 15260 | 16555 | 4165 | 9147 | 10154 | 13133 | 199099 |
| 4 | 2014 | 18870 | 1580 | 4710 | 285 | 690 | 860 | 1380 | 550 | 1010 | 2350 | 6050 | 15940 | 1620 | 3600 | 245 | 720 | 800 | 1180 | 600 | 690 | 2400 | 4680 | 641.8 | 2899.4 | 77.9 | 6.0 | 73.2 | 2257.6 | 133.69175 | 154.13400 | 220.14950 | 143.16750 | 165.08475 | 144.57075 | 141.19150 | 188.93475 | 140.00225 | 111.70625 | 121.78200 | 230.17750 | 106.67700 | 129.58350 | 162.81525 | 95.91100 | 181.68300 | 183.42750 | 128.40525 | 121.79475 | 135.62400 | 115.74175 | 207.52175 | 180.00800 | 132.23875 | 131.47725 | 223.29900 | 249.28800 | 120.81950 | 142.89900 | 209.12425 | 158.86300 | 167.51250 | 138.88250 | 162.42250 | 3392.5 | 8820.0 | 11590.0 | 13025.0 | 13767.5 | 13877.5 | 13655.0 | 13475.0 | 13017.5 | 12245.0 | 9032.5 | 11925.0 | 5363.0 | 75444.0 | 18690.0 | 11030.0 | 1474.0 | 6044.0 | 8508.0 | 8558.0 | 28348.0 | 1548.0 | 28198.0 | 9224.0 | 5075.0 | 2224.0 | 3873.0 | 163457.0 | 50143.0 | 213600.0 | 13111 | 6842 | 14266 | 15619 | 8209 | 23828 | 7424 | 12398 | 11592 | 9987 | 4368 | 1336 | 3034 | 10356 | 13731 | 12265 | 16128 | 16147 | 17763 | 4283 | 9399 | 10503 | 13473 | 213600 |
| 5 | 2015 | 18550 | 1920 | 4540 | 180 | 780 | 720 | 1340 | 485 | 770 | 2440 | 5750 | 16200 | 1390 | 3960 | 205 | 800 | 780 | 1240 | 550 | 970 | 2510 | 4920 | 625.3 | 2953.7 | 78.8 | 5.6 | 74.4 | 2328.3 | 145.30325 | 172.99950 | 209.96075 | 155.77475 | 165.05950 | 143.59000 | 150.65925 | 198.53075 | 137.80175 | 107.15200 | 132.47975 | 241.83900 | 129.02475 | 136.66400 | 171.36475 | 84.66750 | 188.76625 | 170.08400 | 129.65700 | 125.58400 | 142.62450 | 117.33000 | 215.30150 | 185.26900 | 139.21500 | 135.11000 | 226.86450 | 258.14750 | 122.26300 | 153.56575 | 220.05500 | 163.96100 | 173.75725 | 142.87150 | 167.60600 | 3470.0 | 9025.0 | 11745.0 | 13300.0 | 14057.5 | 14255.0 | 14037.5 | 13852.5 | 13335.0 | 12595.0 | 9360.0 | 12192.5 | 5555.0 | 81076.0 | 18754.0 | 11291.0 | 1541.0 | 6176.0 | 8329.0 | 8681.0 | 29588.0 | 1479.0 | 29434.0 | 9427.0 | 4841.0 | 2338.0 | 3996.0 | 170990.0 | 51516.0 | 222506.0 | 8240 | 6936 | 14531 | 18113 | 8753 | 26866 | 7595 | 13833 | 12486 | 10491 | 4763 | 1364 | 3420 | 11424 | 14452 | 13514 | 16526 | 17148 | 18570 | 4556 | 9824 | 10998 | 14263 | 222506 |
| 6 | 2016 | 18440 | 2120 | 5510 | 285 | 730 | 1000 | 1560 | 585 | 830 | 2820 | 5990 | 14920 | 2220 | 4270 | 285 | 700 | 800 | 1270 | 600 | 880 | 2130 | 5350 | 646.8 | 3022.5 | 78.6 | 5.6 | 74.2 | 2375.8 | 165.82600 | 216.45450 | 201.01200 | 185.86800 | 197.03000 | 155.69875 | 170.62350 | 227.02200 | 159.73250 | 118.36025 | 147.28575 | 278.17775 | 139.57950 | 144.00600 | 196.75375 | 84.84350 | 219.53000 | 196.57500 | 141.57025 | 154.53600 | 158.96325 | 124.94600 | 250.19975 | 201.40250 | 156.16900 | 144.60050 | 269.36675 | 312.98700 | 131.72300 | 172.76600 | 252.50325 | 181.38500 | 202.33150 | 157.08750 | 188.63650 | 3570.0 | 9267.5 | 11977.5 | 13605.0 | 14405.0 | 14702.5 | 14470.0 | 14267.5 | 13745.0 | 12967.5 | 9682.5 | 12505.0 | 5994.0 | 88139.0 | 19413.0 | 12287.0 | 1638.0 | 6444.0 | 7221.0 | 8944.0 | 30654.0 | 1380.0 | 30338.0 | 10057.0 | 4693.0 | 2440.0 | 4216.0 | 180735.0 | 53124.0 | 233859.0 | 7691 | 6107 | 13779 | 19703 | 9105 | 28808 | 7673 | 15170 | 12856 | 11083 | 5327 | 1560 | 3745 | 12430 | 15012 | 13962 | 17919 | 18106 | 19932 | 4889 | 10322 | 11431 | 15164 | 233859 |
| 7 | 2017 | 22050 | 2090 | 5140 | 265 | 950 | 1120 | 1640 | 655 | 910 | 2640 | 8000 | 15250 | 1710 | 4870 | 260 | 710 | 860 | 1200 | 590 | 630 | 2260 | 6650 | 608.7 | 3103.3 | 80.4 | 5.3 | 76.1 | 2494.7 | 175.23000 | 243.52650 | 211.83100 | 214.93775 | 250.45875 | 179.49775 | 211.60125 | 282.45575 | 195.68175 | 127.82375 | 148.99275 | 310.85125 | 149.96350 | 158.51150 | 214.95475 | 77.32675 | 279.99425 | 241.28300 | 147.70400 | 228.61125 | 170.49775 | 130.92325 | 269.98625 | 214.66600 | 163.59300 | 159.66025 | 348.40925 | 390.85125 | 139.53325 | 182.87725 | 272.59550 | 207.35825 | 232.79000 | 166.67750 | 216.22275 | 3822.5 | 9607.5 | 12285.0 | 13912.5 | 14750.0 | 15095.0 | 14952.5 | 14665.0 | 14147.5 | 13327.5 | 9995.0 | 12832.5 | 6428.0 | 93624.0 | 20709.0 | 13820.0 | 1707.0 | 7011.0 | 7279.0 | 9358.0 | 32240.0 | 1450.0 | 31189.0 | 10745.0 | 5122.0 | 2604.0 | 4492.0 | 192176.0 | 55602.0 | 247778.0 | 11108 | 6438 | 14320 | 17562 | 9820 | 27381 | 7882 | 16763 | 12843 | 12294 | 5947 | 1830 | 4213 | 12263 | 15447 | 14487 | 19690 | 19297 | 21780 | 5330 | 10902 | 11924 | 15905 | 247778 |
| 8 | 2018 | 22660 | 2360 | 6720 | 290 | 860 | 970 | 1720 | 590 | 880 | 2940 | 7220 | 17380 | 2060 | 5530 | 217 | 730 | 1150 | 1560 | 485 | 770 | 2680 | 6270 | 600.8 | 3165.2 | 81.0 | 4.8 | 77.1 | 2564.3 | 178.72850 | 283.24475 | 211.24675 | 258.77075 | 284.16925 | 210.15675 | 243.12150 | 327.30875 | 224.37100 | 139.30525 | 156.54725 | 319.92250 | 155.55325 | 189.20975 | 231.89250 | 86.00700 | 302.97500 | 271.96825 | 152.39075 | 249.15500 | 177.33800 | 140.40925 | 275.51950 | 234.19450 | 171.59100 | 166.48125 | 376.26800 | 453.76300 | 150.22225 | 187.26825 | 277.11225 | 222.08300 | 257.69600 | 175.38925 | 234.23950 | 4052.5 | 10072.5 | 12815.0 | 14397.5 | 15257.5 | 15665.0 | 15582.5 | 15247.5 | 14727.5 | 13915.0 | 10507.5 | 13357.5 | 6970.0 | 99794.0 | 22583.0 | 15106.0 | 1844.0 | 7567.0 | 8129.0 | 10123.0 | 33610.0 | 1632.0 | 33555.0 | 11754.0 | 5609.0 | 2798.0 | 4835.0 | 205727.0 | 60184.0 | 265911.0 | 12610 | 7422 | 15426 | 19546 | 10092 | 29638 | 8004 | 19009 | 13977 | 12896 | 6247 | 1922 | 4440 | 12909 | 15889 | 16262 | 20538 | 20772 | 23382 | 5728 | 11531 | 12275 | 16708 | 265911 |
| 9 | 2019 | 19170 | 2290 | 5700 | 270 | 900 | 980 | 1820 | 725 | 1010 | 3260 | 6840 | 20200 | 2130 | 4860 | 225 | 920 | 980 | 1630 | 690 | 920 | 2600 | 6690 | 608.7 | 3219.5 | 81.1 | 4.5 | 77.5 | 2610.9 | 170.89575 | 290.04425 | 197.05425 | 286.09400 | 286.96150 | 212.75400 | 235.37500 | 340.89400 | 224.38775 | 148.06575 | 152.72900 | 310.46125 | 152.83000 | 194.33425 | 226.29225 | 91.44775 | 286.35175 | 268.00600 | 147.67325 | 282.87050 | 173.58700 | 141.81725 | 267.49075 | 244.36825 | 169.20850 | 154.99575 | 349.33125 | 473.02500 | 150.12700 | 187.57650 | 273.23250 | 209.39475 | 263.28000 | 176.35950 | 226.13875 | 4252.5 | 10460.0 | 13295.0 | 14860.0 | 15780.0 | 16160.0 | 16220.0 | 15822.5 | 15237.5 | 14417.5 | 11020.0 | 13852.5 | 7238.0 | 105433.0 | 24349.0 | 16322.0 | 1999.0 | 7886.0 | 8245.0 | 10744.0 | 34849.0 | 1685.0 | 35066.0 | 12484.0 | 5932.0 | 2966.0 | 5162.0 | 217065.0 | 63295.0 | 280360.0 | 12655 | 7652 | 16141 | 19888 | 10567 | 30455 | 8489 | 20314 | 14394 | 13503 | 6471 | 2105 | 4609 | 13609 | 16734 | 17161 | 21988 | 22127 | 25041 | 6116 | 12279 | 13099 | 18030 | 280360 |
| 10 | 2020 | 20070 | 1950 | 5130 | 250 | 940 | 990 | 1470 | 680 | 1090 | 2800 | 6460 | 17190 | 1740 | 4740 | 175 | 750 | 1010 | 1410 | 570 | 1000 | 2400 | 5170 | 622.2 | 3272.4 | 81.0 | 4.3 | 77.5 | 2650.3 | 146.18475 | 271.09825 | 167.69850 | 316.91225 | 272.20025 | 218.50475 | 242.59325 | 294.45425 | 207.24825 | 139.34550 | 134.22625 | 288.20650 | 141.80250 | 193.32050 | 193.00775 | 77.95150 | 267.99125 | 261.99975 | 123.96100 | 272.39275 | 156.77425 | 128.82050 | 247.31050 | 216.38300 | 132.52175 | 130.04775 | 342.76125 | 454.21950 | 136.42800 | 165.02750 | 256.37650 | 181.03125 | 236.54725 | 154.66700 | 199.12575 | 4595.0 | 10627.5 | 13565.0 | 15062.5 | 15942.5 | 16417.5 | 16535.0 | 16147.5 | 15552.5 | 14747.5 | 11495.0 | 14167.5 | 7712.0 | 110312.0 | 25993.0 | 17283.0 | 2056.0 | 8397.0 | 8483.0 | 11344.0 | 37561.0 | 1725.0 | 37099.0 | 13097.0 | 6093.0 | 3135.0 | 5487.0 | 229139.0 | 66638.0 | 295777.0 | 13866 | 7383 | 16375 | 20524 | 10733 | 31257 | 8992 | 22400 | 14558 | 13983 | 6823 | 2140 | 4875 | 13817 | 17359 | 17594 | 23654 | 23810 | 27100 | 6289 | 13457 | 13795 | 19447 | 295777 |
| 11 | 2021 | 22980 | 3300 | 6180 | 270 | 1390 | 1090 | 1780 | 790 | 1160 | 3800 | 7750 | 16450 | 2340 | 4950 | 185 | 770 | 930 | 1390 | 660 | 860 | 2800 | 5470 | 643.7 | 3318.6 | 80.6 | 4.9 | 76.6 | 2674.9 | 189.51575 | 392.75500 | 248.80475 | 451.37950 | 381.41450 | 306.77775 | 344.60875 | 396.52100 | 291.07275 | 195.25350 | 177.00750 | 389.31900 | 197.62975 | 294.09700 | 233.62625 | 115.64900 | 401.49900 | 338.09400 | 157.81250 | 369.92700 | 191.84925 | 180.05150 | 329.80450 | 282.02625 | 194.41750 | 180.38150 | 487.17575 | 639.70600 | 183.62100 | 209.55175 | 336.20350 | 257.89600 | 337.49750 | 211.00625 | 282.83275 | 4875.0 | 11410.0 | 14522.5 | 16010.0 | 16915.0 | 17427.5 | 17552.5 | 17115.0 | 16440.0 | 15597.5 | 12175.0 | 15062.5 | 7804.0 | 113290.0 | 26954.0 | 18012.0 | 2256.0 | 8878.0 | 8077.0 | 12115.0 | 37606.0 | 1784.0 | 37011.0 | 12860.0 | 6180.0 | 3197.0 | 5582.0 | 234991.0 | 66613.0 | 301604.0 | 14319 | 6645 | 15200 | 19432 | 11376 | 30808 | 8555 | 23294 | 15958 | 15235 | 6002 | 1557 | 4255 | 11724 | 17244 | 18121 | 24686 | 25464 | 28022 | 5633 | 14219 | 14507 | 21356 | 301604 |
| 12 | 2022 | 23740 | 2460 | 5800 | 265 | 940 | 1130 | 1870 | 620 | 1160 | 3500 | 6770 | 17440 | 2070 | 4100 | 345 | 770 | 890 | 1510 | 570 | 1110 | 2720 | 5670 | 603.1 | 3310.2 | 81.8 | 3.6 | 78.8 | 2707.1 | 200.32850 | 429.89125 | 286.85100 | 459.61800 | 394.09675 | 342.01750 | 402.06400 | 457.42375 | 302.15075 | 192.75275 | 188.98575 | 391.05925 | 213.92500 | 333.00550 | 338.50825 | 107.83000 | 437.89625 | 342.50450 | 173.08300 | 368.58675 | 188.49375 | 186.20775 | 348.89025 | 362.15575 | 208.88800 | 201.23075 | 492.31925 | 695.99400 | 185.33725 | 241.53550 | 342.63600 | 281.00425 | 386.71325 | 217.79625 | 314.24425 | 5285.0 | 12522.5 | 15717.5 | 17320.0 | 18235.0 | 18725.0 | 18897.5 | 18440.0 | 17642.5 | 16700.0 | 13062.5 | 16185.0 | 8684.0 | 124270.0 | 29378.0 | 19527.0 | 2318.0 | 9773.0 | 8911.0 | 13110.0 | 40777.0 | 1798.0 | 40380.0 | 14016.0 | 7144.0 | 3283.0 | 6038.0 | 256748.0 | 72659.0 | 329407.0 | 16373 | 6805 | 16463 | 19567 | 12030 | 31597 | 9658 | 24320 | 18992 | 16985 | 6851 | 1743 | 4896 | 13124 | 18604 | 20252 | 26033 | 27240 | 31774 | 6671 | 15185 | 15489 | 23665 | 329407 |
2.4 Big Data Processing OptimizationΒΆ
'Job market' and 'GDP' related data have been merged into a single master table (merged_data). Given the size of this table, the following strategies are planned to optimise data processing performance:
- Convert to Dask DataFrame:
- This approach greatly improves processing efficiency by distributing data processing across multiple cores.
- Changing data types:
- Memory usage can be halved by converting float64,int64 types to float32,int32 respectively. In this way, calculation speed can be increased without loss of data accuracy.
- Store data in HDF5 format:
- HDF5 is a high performance data format. Storing data in HDF5 not only increases read/write speeds, but also supports complex queries and efficient data access.
2.4.1 Convert to Dask DataFrameΒΆ
import dask.dataframe as dd
# Convert to Dask DataFrame
ddf = dd.from_pandas(merged_data, npartitions=10)
2.4.2 Changing data typesΒΆ
# Change float64 to float32. Also change int64 to int32.
for column in merged_data.columns:
if merged_data[column].dtype == 'float64':
merged_data[column] = merged_data[column].astype('float32')
if merged_data[column].dtype == 'int64':
merged_data[column] = merged_data[column].astype('int32')
2.4.3 Store data in HDF5 formatΒΆ
It is important to ensure that the column names do not contain any special characters before saving the dataset in HDF5 format.
Therefore, the first step is to adjust the column names.
# Define a function to replace special characters in column names
def clean_column_names(column_name):
return column_name.replace(' ', '_').replace('/', '_').replace("'", "").replace('&', 'and').replace(',', '_').replace('-', '_')
# Apply this function to all columns
merged_data.columns = [clean_column_names(col) for col in merged_data.columns]
After changing all column names, the next step is saving the file in HDF5
# Save dataset to HDF5 file
merged_data.to_hdf('merged_data.h5', key='data', mode='w', format='table', data_columns=True)
3. Data AnalysisΒΆ
3.1 Discuss the overall GDP changes in New Zealand from 2010 to 2022. What are the top three industrial contributions?ΒΆ
3.1.1 GDP Distribution in North Island, South Island and New Zealand from 2010 to 2022ΒΆ
sns.set_theme(style="whitegrid")
plot_data = pd.melt(merged_data, id_vars=['Year'], value_vars=['GDP_Total_North_Island', 'GDP_Total_South_Island', 'GDP_New_Zealand'], var_name='Region', value_name='GDP')
plt.figure(figsize=(14, 7))
sns.lineplot(data=plot_data, x='Year', y='GDP', hue='Region', linewidth=2.5)
plt.xlabel('Year')
plt.ylabel('GDP')
plt.title('GDP Distribution in North Island, South Island and New Zealand from 2010 to 2022')
plt.figtext(0.95, 0.02, 'Source: GDP_region.csv', horizontalalignment='right', verticalalignment='bottom')
Text(0.95, 0.02, 'Source: GDP_region.csv')
The above table shows that GDP in New Zealand has been increasing year by year since 2010, while the GDP of the North Island seems to be higher than the South Island, the gap seems to be very significant.
It may because Auckland and Wellington and other big cities are located in the North Island.
3.1.2 GDP Distribution by Industry in New Zealand from 2010 to 2022ΒΆ
columns_choose2 = [
'GDP_Agriculture',
'GDP_Forestry__Fishing__and_Mining',
'GDP_Forestry__Fishing__Mining__Electricity__Gas__Water_and_Waste_Services',
'GDP_Manufacturing',
'GDP_Electricity__Gas__Water__and_Waste_services',
'GDP_Construction',
'GDP_Wholesale_Trade',
'GDP_Retail_Trade',
'GDP_Accommodation_and_Food_Services',
'GDP_Accommodation',
'GDP_Food_and_beverage_services',
'GDP_Transport__Postal_and_Warehousing',
'GDP_Information_Media__Telecommunications_and_Other_Services',
'GDP_Financial_and_Insurance_Services',
'GDP_Rental__Hiring_and_Real_Estate_Services',
'GDP_Owner_Occupied_Property_Operation',
'GDP_Professional__Scientific__and_Technical_Services',
'GDP_Administrative_and_Support_Services',
'GDP_Public_Administration_and_Safety',
'GDP_Education_and_Training',
'GDP_Health_Care_and_Social_Assistance']
years = merged_data['Year']
vals = [merged_data[col].values for col in columns_choose2]
plt.figure(figsize=(16, 9), dpi=80)
colors = [plt.cm.Spectral(i / float(len(columns_choose2) - 1)) for i in range(len(columns_choose2))]
plt.stackplot(years, vals, labels=columns_choose2, colors=colors)
plt.legend(loc='center left', bbox_to_anchor=(1, 0.5), frameon=True)
plt.title('GDP Distribution by Industry in New Zealand from 2010 to 2022', fontsize=22)
plt.xlabel('Year')
plt.ylabel('GDP')
plt.xticks(rotation=45)
plt.figtext(0.95, 0.02, 'Source: industryGDP.csv', horizontalalignment='right', verticalalignment='bottom')
Text(0.95, 0.02, 'Source: industryGDP.csv')
According to the chart, we can see that there are 24 industries in the industry GDP file, manufacturing and professional scientific and technical services seem to have the largest proportion.
The whole industry seems to be growing steadily, and there is no particularly huge decline or rise.
3.1.3 Finding the Top 3 Contribution Industries to New Zealand GDP for 2022-2023ΒΆ
plt.figure(figsize=(14, 5))
plt.plot(merged_industrycontribution['Industry'], merged_industrycontribution['GDP_2022(M)'], label='Contribution of GDP 2022')
plt.plot(merged_industrycontribution['Industry'], merged_industrycontribution['GDP_2023(M)'], label='Contribution of GDP 2023')
average_gdp_2022 = merged_industrycontribution['GDP_2022(M)'].mean()
average_gdp_2023 = merged_industrycontribution['GDP_2023(M)'].mean()
plt.axhline(y=average_gdp_2022, color='skyblue', linestyle='--', label='Average GDP 2022')
plt.axhline(y=average_gdp_2023, color='orange', linestyle='--', label='Average GDP 2023')
plt.ylabel('GDP')
plt.title("Contribution of Each Industry to New Zealand GDP in 2022-2023")
plt.xticks(rotation=45,fontsize=9)
plt.legend()
plt.subplots_adjust(bottom=0.30, right=0.85)
plt.figtext(0.95, 0.02, 'Source: API https://rep.infometrics.co.nz ', horizontalalignment='right', verticalalignment='bottom')
Text(0.95, 0.02, 'Source: API https://rep.infometrics.co.nz ')
Based on the table, we can identify the following insights about the GDP contributions for New Zealand in 2022-2023. The figures for these two years are very close, indicating that there have been no significant changes across the various industries.
The highest contributing industry is the professional, scientific, and technical industry. The second highest is manufacturing. The third highest is health care and social assistance.
The lowest contributing industries are mining and art, recreation, likely due to the lack of mineral resources in New Zealand.
In terms of the median contribution, agriculture, forestry, and fishing and Public administration and safety are in the middle range, indicating that their contribution to New Zealand's GDP is moderate, not too high and not too low.
We found the highest contributing industry is the professional, scientific, and technical industry, which we found very interesting because it is also the field we are currently studying. Our subsequent analysis will continue to explore more aspects.
3.2 Does the Industry that Contributes the Most GDP Bring the Same Proportion of Job Opportunities?ΒΆ
3.2.1 Adding Employment Job Numbers Variable and Analyzing Their Relationship to GDP ProportionΒΆ
plt.figure(figsize=(14, 5))
plt.plot(merged_industrycontribution['Industry'], merged_industrycontribution['GDP_2022(M)'], label='Contribution of GDP 2022')
plt.plot(merged_industrycontribution['Industry'], merged_industrycontribution['GDP_2023(M)'], label='Contribution of GDP 2023')
plt.plot(merged_industrycontribution['Industry'], merged_industrycontribution['Employment_2022'],color='Green', linestyle='--', label='Jobs Numbers 2022')
plt.plot(merged_industrycontribution['Industry'], merged_industrycontribution['Employment_2023'], color='Green',label='Jobs Numbers 2023')
plt.title("Contribution of Each Industry to New Zealand GDP and Job Numbers in 2022-2023")
plt.xticks(rotation=45,fontsize=9)
plt.legend(fontsize=10, loc='best', bbox_to_anchor=(1, 1))
plt.subplots_adjust(bottom=0.30, right=0.85)
plt.figtext(0.95, 0.02, 'Source: API https://rep.infometrics.co.nz ', horizontalalignment='right', verticalalignment='bottom')
Text(0.95, 0.02, 'Source: API https://rep.infometrics.co.nz ')
We found something very interesting! The top three contributing industries to GDP are Professional, scientific, and technical services, Manufacturing, and Health care and social assistance.However, these do not directly correspond to the industries providing the most job opportunities. From the table, we can see that the industries providing the most jobs are Construction, Health Care and Social Assistance, and Professional, Scientific, and Technical Services.
This indicates that there is not a direct correlation between industry GDP contributions and the number of job opportunities in New Zealand. There are several possible reasons why Professional, Scientific, and Technical Services, as well as Manufacturing, contribute more to GDP: Professional, Scientific, and Technical Services and Manufacturing industries typically rely on high skills and high productivity. They may have higher capital investment and technical equipment, enabling each employee to generate more value. Professionals usually have high levels of skill and knowledge, resulting in higher wages. Higher wages translate into higher GDP contributions. Manufacturing is highly dependent on the export market. The products produced may be sold globally, bringing in significant income and GDP contribution, rather than relying solely on the local market.
When examining the relationship between GDP and employment in each sector, we noticed a particular trend:
Transportation, Postal, and Warehousing contribute significantly to GDP, but the number of jobs in these sectors is very low. This may be due to automation and technological advancements that have increased productivity, thus significantly reducing the need for human labor.
3.3 Based on the Occupation Listed on Careers.NZ. What's the Job Opportunities in 6 Main Industries?ΒΆ
3.3.1 Job Opportunities by 6 Main Industries in New Zealand (Lastest DATA)ΒΆ
summary_table
| Job Opportunities | Average | Good | Poor |
|---|---|---|---|
| Industry | |||
| Construction and infrastructure | 16 | 40 | 3 |
| Creative industries | 22 | 7 | 29 |
| Manufacturing and technology | 25 | 64 | 11 |
| Primary industries | 22 | 34 | 3 |
| Services industries | 61 | 75 | 30 |
| Social and community services | 29 | 66 | 15 |
plt.figure(figsize=(14, 7))
width = 0.3
x = np.arange(len(summary_table.index))
plt.bar(x - width, summary_table['Good'], width=width, label='Good',color='tan')
plt.bar(x, summary_table['Average'], width=width, label='Average',color='peru')
plt.bar(x + width, summary_table['Poor'], width=width, label='Poor',color='maroon')
plt.xlabel('Vocations')
plt.ylabel('Classified Numbers')
plt.title(' Job Opportunities by 6 Main Industries in New Zealand')
plt.xticks(x, summary_table.index, rotation=45, ha='right', fontsize=12)
plt.legend(title='Job Opportunities')
plt.subplots_adjust(bottom=0.30, right=0.85)
plt.figtext(0.95, 0.02, 'Source: Web Scraping https://www.careers.govt.nz', horizontalalignment='right', verticalalignment='bottom')
Text(0.95, 0.02, 'Source: Web Scraping https://www.careers.govt.nz')
There are over 60 types of jobs categorized as "Good" in Manufacturing and Technology which is outstanding compared to other industries. Overall, the prospects for Manufacturing and Technology look quite promising.
Construction and Infrastructure seem to have fewer job types overall, but more than half are in the "Good" category.
The Creative Industry has fewer job types overall, and most of them fall into the "Poor" category, indicating that the prospects for the Creative Industry are somewhat bleak.
The Service Industry has the highest proportion of "Average" jobs and also the most job types overall.
3.3.2 Job Opportunities by 6 Main Industries in New Zealand (Lastest DATA) - AccumulatedΒΆ
plt.figure(figsize=(14, 7))
colors = sns.color_palette(['tan', 'peru', 'maroon'])
p1 = plt.bar(summary_table.index, summary_table['Good'], color=colors[0], label='Good')
p2 = plt.bar(summary_table.index, summary_table['Average'], bottom=summary_table['Good'], color=colors[1], label='Average')
p3 = plt.bar(summary_table.index, summary_table['Poor'], bottom=summary_table['Good'] + summary_table['Average'], color=colors[2], label='Poor')
plt.xlabel('Vocations')
plt.ylabel('Classified Numbers')
plt.title('Job Opportunities by 6 Main Industries in New Zealand- Accumulated')
plt.xticks(rotation=45, ha='right', fontsize=12)
plt.legend(title='Job Opportunities')
plt.subplots_adjust(bottom=0.30, right=0.85)
plt.figtext(0.95, 0.02, 'Source: Web Scraping https://www.careers.govt.nz', horizontalalignment='right', verticalalignment='bottom')
Text(0.95, 0.02, 'Source: Web Scraping https://www.careers.govt.nz')
Social and Community Service provides the second-highest number of job types. Interestingly, this aligns with the 3.2.1 table, which indicated that Social Assistance and Professional Services offer the second-most job opportunities.
β Note: The above source listed in each graph shows various methods we used to collect data. From below graph, the source of each table's data will not be specifically indicated.ΒΆ
3.4 As the Largest City in New Zealand and a Major Economic Hub, is Auckland the Best in Terms of All Vacancy Indices (AVIs) and GDP from 2010 to 2022?ΒΆ
3.4.1 All Vacancy Indices (AVIs) by Region from 2010-2022ΒΆ
job_online
| Year | AVI_Auckland | AVI_Bay of Plenty | AVI_Canterbury | AVI_Gisborne Hawke's Bay | AVI_Marlborough/NelsonTasman/West Coast | AVI_Manawatu/Wanganui-Taranaki | AVI_Northland | AVI_Otago Southland | AVI_Waikato | AVI_Wellington | AVI_ids_Accounting | AVI_ids_Construction | AVI_ids_Education | AVI_ids_Health | AVI_ids_Hospitalty | AVI_ids_IT | AVI_ids_Manufacturing | AVI_ids_Primary | AVI_ids_Sales | AVI_ids_Other | AVI_ocp_Managers | AVI_ocp_Professionals | AVI_ocp_Tech & Trades | AVI_ocp_Community & Personal Services | AVI_ocp_Clerical & Administration | AVI_ocp_Sales | AVI_ocp_Machinery Drivers | AVI_ocp_Labourers | AVI_ocp_highly skilled | AVI_ocp_skilled | AVI_ocp_semi-skilled | AVI_ocp_low skilled | AVI_ocp_unskilled | AVI_ocp_Skilled | AVI_ocp_Unskilled | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2010 | 100.00000 | 100.00000 | 100.00000 | 100.00000 | 100.00000 | 100.00000 | 100.00000 | 100.00000 | 100.00000 | 100.00000 | 100.00000 | 100.00000 | 100.00000 | 100.00000 | 100.00000 | 100.00000 | 100.00000 | 100.00000 | 100.00000 | 100.00000 | 100.00000 | 100.00000 | 100.00000 | 100.00000 | 100.00000 | 100.00000 | 100.00000 | 100.00000 | 100.00000 | 100.00000 | 100.00000 | 100.00000 | 100.00000 | 100.00000 | 100.00000 |
| 1 | 2011 | 101.53725 | 103.78475 | 131.81150 | 107.87775 | 111.24925 | 104.01875 | 109.50200 | 116.04450 | 105.59650 | 101.32300 | 106.24775 | 115.75200 | 92.19875 | 104.26875 | 107.14425 | 102.21425 | 111.41325 | 123.85425 | 101.98475 | 108.94725 | 103.91725 | 106.69575 | 121.15200 | 115.34075 | 107.32600 | 105.28125 | 113.40850 | 118.31375 | 105.89775 | 103.69375 | 126.95700 | 111.03375 | 104.36725 | 108.66475 | 109.75675 |
| 2 | 2012 | 104.07175 | 110.63300 | 170.32750 | 120.65275 | 129.16900 | 117.80575 | 115.17250 | 138.57875 | 110.88150 | 102.74725 | 109.49900 | 147.46800 | 83.28000 | 119.27800 | 112.45725 | 97.90175 | 125.17300 | 148.83975 | 105.11225 | 113.70325 | 106.71575 | 107.81950 | 142.38200 | 133.33875 | 113.67750 | 107.55275 | 137.17150 | 145.01325 | 107.70150 | 110.10950 | 150.74725 | 120.93250 | 116.90500 | 113.88775 | 120.46675 |
| 3 | 2013 | 116.27650 | 130.56400 | 191.61625 | 127.15625 | 136.18075 | 137.26350 | 120.30400 | 150.94075 | 123.46825 | 106.19475 | 112.27775 | 184.80025 | 92.26350 | 123.59450 | 134.52225 | 92.37850 | 153.48125 | 158.91900 | 113.93700 | 122.01075 | 116.59175 | 108.56500 | 171.24675 | 157.54900 | 120.54600 | 118.16900 | 180.68500 | 190.80675 | 110.85300 | 123.29000 | 181.36625 | 137.46225 | 138.64800 | 123.33150 | 138.68850 |
| 4 | 2014 | 133.69175 | 154.13400 | 220.14950 | 143.16750 | 165.08475 | 144.57075 | 141.19150 | 188.93475 | 140.00225 | 111.70625 | 121.78200 | 230.17750 | 106.67700 | 129.58350 | 162.81525 | 95.91100 | 181.68300 | 183.42750 | 128.40525 | 121.79475 | 135.62400 | 115.74175 | 207.52175 | 180.00800 | 132.23875 | 131.47725 | 223.29900 | 249.28800 | 120.81950 | 142.89900 | 209.12425 | 158.86300 | 167.51250 | 138.88250 | 162.42250 |
| 5 | 2015 | 145.30325 | 172.99950 | 209.96075 | 155.77475 | 165.05950 | 143.59000 | 150.65925 | 198.53075 | 137.80175 | 107.15200 | 132.47975 | 241.83900 | 129.02475 | 136.66400 | 171.36475 | 84.66750 | 188.76625 | 170.08400 | 129.65700 | 125.58400 | 142.62450 | 117.33000 | 215.30150 | 185.26900 | 139.21500 | 135.11000 | 226.86450 | 258.14750 | 122.26300 | 153.56575 | 220.05500 | 163.96100 | 173.75725 | 142.87150 | 167.60600 |
| 6 | 2016 | 165.82600 | 216.45450 | 201.01200 | 185.86800 | 197.03000 | 155.69875 | 170.62350 | 227.02200 | 159.73250 | 118.36025 | 147.28575 | 278.17775 | 139.57950 | 144.00600 | 196.75375 | 84.84350 | 219.53000 | 196.57500 | 141.57025 | 154.53600 | 158.96325 | 124.94600 | 250.19975 | 201.40250 | 156.16900 | 144.60050 | 269.36675 | 312.98700 | 131.72300 | 172.76600 | 252.50325 | 181.38500 | 202.33150 | 157.08750 | 188.63650 |
| 7 | 2017 | 175.23000 | 243.52650 | 211.83100 | 214.93775 | 250.45875 | 179.49775 | 211.60125 | 282.45575 | 195.68175 | 127.82375 | 148.99275 | 310.85125 | 149.96350 | 158.51150 | 214.95475 | 77.32675 | 279.99425 | 241.28300 | 147.70400 | 228.61125 | 170.49775 | 130.92325 | 269.98625 | 214.66600 | 163.59300 | 159.66025 | 348.40925 | 390.85125 | 139.53325 | 182.87725 | 272.59550 | 207.35825 | 232.79000 | 166.67750 | 216.22275 |
| 8 | 2018 | 178.72850 | 283.24475 | 211.24675 | 258.77075 | 284.16925 | 210.15675 | 243.12150 | 327.30875 | 224.37100 | 139.30525 | 156.54725 | 319.92250 | 155.55325 | 189.20975 | 231.89250 | 86.00700 | 302.97500 | 271.96825 | 152.39075 | 249.15500 | 177.33800 | 140.40925 | 275.51950 | 234.19450 | 171.59100 | 166.48125 | 376.26800 | 453.76300 | 150.22225 | 187.26825 | 277.11225 | 222.08300 | 257.69600 | 175.38925 | 234.23950 |
| 9 | 2019 | 170.89575 | 290.04425 | 197.05425 | 286.09400 | 286.96150 | 212.75400 | 235.37500 | 340.89400 | 224.38775 | 148.06575 | 152.72900 | 310.46125 | 152.83000 | 194.33425 | 226.29225 | 91.44775 | 286.35175 | 268.00600 | 147.67325 | 282.87050 | 173.58700 | 141.81725 | 267.49075 | 244.36825 | 169.20850 | 154.99575 | 349.33125 | 473.02500 | 150.12700 | 187.57650 | 273.23250 | 209.39475 | 263.28000 | 176.35950 | 226.13875 |
| 10 | 2020 | 146.18475 | 271.09825 | 167.69850 | 316.91225 | 272.20025 | 218.50475 | 242.59325 | 294.45425 | 207.24825 | 139.34550 | 134.22625 | 288.20650 | 141.80250 | 193.32050 | 193.00775 | 77.95150 | 267.99125 | 261.99975 | 123.96100 | 272.39275 | 156.77425 | 128.82050 | 247.31050 | 216.38300 | 132.52175 | 130.04775 | 342.76125 | 454.21950 | 136.42800 | 165.02750 | 256.37650 | 181.03125 | 236.54725 | 154.66700 | 199.12575 |
| 11 | 2021 | 189.51575 | 392.75500 | 248.80475 | 451.37950 | 381.41450 | 306.77775 | 344.60875 | 396.52100 | 291.07275 | 195.25350 | 177.00750 | 389.31900 | 197.62975 | 294.09700 | 233.62625 | 115.64900 | 401.49900 | 338.09400 | 157.81250 | 369.92700 | 191.84925 | 180.05150 | 329.80450 | 282.02625 | 194.41750 | 180.38150 | 487.17575 | 639.70600 | 183.62100 | 209.55175 | 336.20350 | 257.89600 | 337.49750 | 211.00625 | 282.83275 |
| 12 | 2022 | 200.32850 | 429.89125 | 286.85100 | 459.61800 | 394.09675 | 342.01750 | 402.06400 | 457.42375 | 302.15075 | 192.75275 | 188.98575 | 391.05925 | 213.92500 | 333.00550 | 338.50825 | 107.83000 | 437.89625 | 342.50450 | 173.08300 | 368.58675 | 188.49375 | 186.20775 | 348.89025 | 362.15575 | 208.88800 | 201.23075 | 492.31925 | 695.99400 | 185.33725 | 241.53550 | 342.63600 | 281.00425 | 386.71325 | 217.79625 | 314.24425 |
| 13 | 2023 | 150.42225 | 316.88425 | 226.37375 | 391.14925 | 318.20275 | 273.39400 | 333.16950 | 376.89850 | 230.76125 | 149.42500 | 153.03725 | 293.48275 | 225.12700 | 291.17150 | 242.09025 | 70.66075 | 303.14050 | 281.37575 | 130.16425 | 284.00675 | 153.23075 | 154.13150 | 261.71650 | 287.60175 | 159.32575 | 149.50925 | 355.24425 | 458.83100 | 148.58200 | 205.38975 | 265.21225 | 207.04550 | 266.91375 | 175.83525 | 226.94200 |
| 14 | 2024 | 125.56600 | 265.24100 | 185.86700 | 368.91800 | 266.95100 | 231.45900 | 293.04300 | 332.75100 | 198.37200 | 110.94500 | 127.45200 | 246.27800 | 208.19800 | 265.36500 | 205.14200 | 52.28700 | 234.85900 | 236.81800 | 109.07700 | 226.68000 | 131.48600 | 128.65100 | 227.27200 | 241.57000 | 132.47300 | 126.55200 | 280.04200 | 350.80000 | 122.93600 | 184.20400 | 227.88300 | 169.97600 | 208.36100 | 147.56200 | 182.64700 |
_ = job_online.hist(bins=int(np.sqrt(len(job_online))),facecolor='grey', figsize=(18, 18), xlabelsize=8)
Showing the freqeuncy of AVI dataset, giving the overall idea about how the data distributed.
avi_columns = ['AVI_Auckland', 'AVI_Bay_of_Plenty', 'AVI_Canterbury', 'AVI_Gisborne_Hawkes_Bay',
'AVI_Marlborough_NelsonTasman_West_Coast', 'AVI_Manawatu_Wanganui_Taranaki',
'AVI_Northland', 'AVI_Otago_Southland', 'AVI_Waikato', 'AVI_Wellington']
plot_data = merged_data.melt(id_vars=['Year'], value_vars=avi_columns, var_name='Variable', value_name='Value')
g = sns.relplot(x='Year', y='Value', hue='Variable', size='Value', sizes=(40, 400), alpha=.5, palette="bright", height=6, data=plot_data, aspect=2)
g.set_axis_labels("Year", "AVI")
g.fig.suptitle("All Vacancy Indices (AVIs) by Region from 2010-2022", fontsize=16)
C:\Users\Alene\anaconda3\Lib\site-packages\seaborn\axisgrid.py:118: UserWarning: The figure layout has changed to tight self._figure.tight_layout(*args, **kwargs)
Text(0.5, 0.98, 'All Vacancy Indices (AVIs) by Region from 2010-2022')
The All Vacancies Indices (AVI) is an important indicator of the labor market. A higher AVI indicates more job vacancies, which means a better job market with more opportunities. Conversely, a lower AVI might indicate fewer job vacancies. Therefore, a higher AVI is typically considered indicative of a healthier job market. In 2010, all AVIs were set to 100 to make it easier to understand the growth or decline for each region in the following years.
Based on the above graph, we can observe that the blue dots representing Auckland's AVI are located around 100 to 200 from 2012 to 2022. Compared to other regions, Auckland does not have the best performance. This might be because the number of jobs in Auckland started from a larger baseline than other regions.
For 2022, the highest AVIs are seen in Bay of Plenty, Gisborne Hawke's Bay, and Otago Southland. This indicates that these regions have significantly increased their job opportunities since 2010.
3.4.2 GDP Contribution by Region from 2010-2022ΒΆ
columns_with_index = {i: col for i, col in enumerate(merged_data.columns)}
for index, column in columns_with_index.items():
print(f"Column {index}: {column}")
Column 0: Year Column 1: job_creation_Auckland Column 2: job_creation_Bay_of_Plenty Column 3: job_creation_Canterbury Column 4: job_creation_Gisborne Column 5: job_creation_Hawkes_Bay Column 6: job_creation_Manawatu_Wanganui Column 7: job_creation_Otago Column 8: job_creation_Taranaki Column 9: job_creation_Tasman__Nelson__Marlborough__West_Coast Column 10: job_creation_Waikato Column 11: job_creation_Wellington Column 12: job_destruction_Auckland Column 13: job_destruction_Bay_of_Plenty Column 14: job_destruction_Canterbury Column 15: job_destruction_Gisborne Column 16: job_destruction_Hawkes_Bay Column 17: job_destruction_Manawatu_Wanganui Column 18: job_destruction_Otago Column 19: job_destruction_Taranaki Column 20: job_destruction_Tasman__Nelson__Marlborough__West_Coast Column 21: job_destruction_Waikato Column 22: job_destruction_Wellington Column 23: Not_in_Labour_Force Column 24: Working_Age_Population Column 25: Labour_Force_Participation_Rate Column 26: Unemployment_Rate Column 27: Employment_Rate Column 28: Total_Labour_Force Column 29: AVI_Auckland Column 30: AVI_Bay_of_Plenty Column 31: AVI_Canterbury Column 32: AVI_Gisborne_Hawkes_Bay Column 33: AVI_Marlborough_NelsonTasman_West_Coast Column 34: AVI_Manawatu_Wanganui_Taranaki Column 35: AVI_Northland Column 36: AVI_Otago_Southland Column 37: AVI_Waikato Column 38: AVI_Wellington Column 39: AVI_ids_Accounting Column 40: AVI_ids_Construction Column 41: AVI_ids_Education Column 42: AVI_ids_Health Column 43: AVI_ids_Hospitalty Column 44: AVI_ids_IT Column 45: AVI_ids_Manufacturing Column 46: AVI_ids_Primary Column 47: AVI_ids_Sales Column 48: AVI_ids_Other Column 49: AVI_ocp_Managers Column 50: AVI_ocp_Professionals Column 51: AVI_ocp_Tech_and_Trades Column 52: AVI_ocp_Community_and_Personal_Services Column 53: AVI_ocp_Clerical_and_Administration Column 54: AVI_ocp_Sales Column 55: AVI_ocp_Machinery_Drivers Column 56: AVI_ocp_Labourers Column 57: AVI_ocp_highly_skilled Column 58: AVI_ocp_skilled Column 59: AVI_ocp_semi_skilled Column 60: AVI_ocp_low_skilled Column 61: AVI_ocp_unskilled Column 62: AVI_ocp_Skilled Column 63: AVI_ocp_Unskilled Column 64: median_wage_15_19 Column 65: median_wage_20_24 Column 66: median_wage_25_29 Column 67: median_wage_30_34 Column 68: median_wage_35_39 Column 69: median_wage_40_44 Column 70: median_wage_45_49 Column 71: median_wage_50_54 Column 72: median_wage_55_59 Column 73: median_wage_60_64 Column 74: median_wage_65 Column 75: median_wage_Total_All_Ages Column 76: GDP_Northland Column 77: GDP_Auckland Column 78: GDP_Waikato Column 79: GDP_Bay_of_Plenty Column 80: GDP_Gisborne Column 81: GDP_Hawkes_Bay Column 82: GDP_Taranaki Column 83: GDP_Manawatu_Whanganui Column 84: GDP_Wellington Column 85: GDP_West_Coast Column 86: GDP_Canterbury Column 87: GDP_Otago Column 88: GDP_Southland Column 89: GDP_Marlborough Column 90: GDP_Tasman_Nelson Column 91: GDP_Total_North_Island Column 92: GDP_Total_South_Island Column 93: GDP_New_Zealand Column 94: GDP_Agriculture Column 95: GDP_Forestry__Fishing__and_Mining Column 96: GDP_Forestry__Fishing__Mining__Electricity__Gas__Water_and_Waste_Services Column 97: GDP_Primary_Manufacturing Column 98: GDP_Other_Manufacturing Column 99: GDP_Manufacturing Column 100: GDP_Electricity__Gas__Water__and_Waste_services Column 101: GDP_Construction Column 102: GDP_Wholesale_Trade Column 103: GDP_Retail_Trade Column 104: GDP_Accommodation_and_Food_Services Column 105: GDP_Accommodation Column 106: GDP_Food_and_beverage_services Column 107: GDP_Transport__Postal_and_Warehousing Column 108: GDP_Information_Media__Telecommunications_and_Other_Services Column 109: GDP_Financial_and_Insurance_Services Column 110: GDP_Rental__Hiring_and_Real_Estate_Services Column 111: GDP_Owner_Occupied_Property_Operation Column 112: GDP_Professional__Scientific__and_Technical_Services Column 113: GDP_Administrative_and_Support_Services Column 114: GDP_Public_Administration_and_Safety Column 115: GDP_Education_and_Training Column 116: GDP_Health_Care_and_Social_Assistance Column 117: GDP_Total_All_Industries
columns_choose = [77, 76, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90]
years = merged_data['Year']
vals = [merged_data.iloc[:, col].values for col in columns_choose]
labels = [merged_data.columns[col] for col in columns_choose]
plt.figure(figsize=(16, 9), dpi=80)
colors = [plt.cm.Spectral(i / float(len(columns_choose) - 1)) for i in range(len(columns_choose))]
plt.stackplot(years, vals, labels=labels, colors=colors)
plt.legend(loc='center left', bbox_to_anchor=(1, 0.5), frameon=True)
plt.title('GDP Contribution by Region from 2010 to 2022', fontsize=22)
plt.xlabel('Year')
plt.ylabel('GDP')
plt.xticks(rotation=45)
(array([2008., 2010., 2012., 2014., 2016., 2018., 2020., 2022., 2024.]), [Text(2008.0, 0, '2008'), Text(2010.0, 0, '2010'), Text(2012.0, 0, '2012'), Text(2014.0, 0, '2014'), Text(2016.0, 0, '2016'), Text(2018.0, 0, '2018'), Text(2020.0, 0, '2020'), Text(2022.0, 0, '2022'), Text(2024.0, 0, '2024')])
Based on GDP contribution graph, Auckland has consistently maintained the highest contribution proportion from 2010 to 2022. As New Zealand's main economic city, Auckland's stable economic growth is supported by the data.
From the above 2 graphs suggest that while Auckland remains crucial for the overall economy, other regions are catching up in terms of job market opportunities.
3.5 How Does Unemployment Rate in New Zealand Relate to Auckland's AVI and GDP?ΒΆ
3.5.1 Create the Classified Column for Unemployment Rate and AVI Auckland by Median.ΒΆ
UnemploymentRate_median = merged_data['Unemployment_Rate'].median()
UnemploymentRate_median
5.599999904632568
AVI_Auckland_median = merged_data['AVI_Auckland'].median()
AVI_Auckland_median
146.18475341796875
unemployment_rate_label = np.where(merged_data['Unemployment_Rate'] > 5.6, 'High', 'Low')
avi_auckland_label = np.where(merged_data['AVI_Auckland'] > 146.18475, 'High', 'Low')
merged_data1 = merged_data.copy()
merged_data1
| Year | job_creation_Auckland | job_creation_Bay_of_Plenty | job_creation_Canterbury | job_creation_Gisborne | job_creation_Hawkes_Bay | job_creation_Manawatu_Wanganui | job_creation_Otago | job_creation_Taranaki | job_creation_Tasman__Nelson__Marlborough__West_Coast | job_creation_Waikato | job_creation_Wellington | job_destruction_Auckland | job_destruction_Bay_of_Plenty | job_destruction_Canterbury | job_destruction_Gisborne | job_destruction_Hawkes_Bay | job_destruction_Manawatu_Wanganui | job_destruction_Otago | job_destruction_Taranaki | job_destruction_Tasman__Nelson__Marlborough__West_Coast | job_destruction_Waikato | job_destruction_Wellington | Not_in_Labour_Force | Working_Age_Population | Labour_Force_Participation_Rate | Unemployment_Rate | Employment_Rate | Total_Labour_Force | AVI_Auckland | AVI_Bay_of_Plenty | AVI_Canterbury | AVI_Gisborne_Hawkes_Bay | AVI_Marlborough_NelsonTasman_West_Coast | AVI_Manawatu_Wanganui_Taranaki | AVI_Northland | AVI_Otago_Southland | AVI_Waikato | AVI_Wellington | AVI_ids_Accounting | AVI_ids_Construction | AVI_ids_Education | AVI_ids_Health | AVI_ids_Hospitalty | AVI_ids_IT | AVI_ids_Manufacturing | AVI_ids_Primary | AVI_ids_Sales | AVI_ids_Other | AVI_ocp_Managers | AVI_ocp_Professionals | AVI_ocp_Tech_and_Trades | AVI_ocp_Community_and_Personal_Services | AVI_ocp_Clerical_and_Administration | AVI_ocp_Sales | AVI_ocp_Machinery_Drivers | AVI_ocp_Labourers | AVI_ocp_highly_skilled | AVI_ocp_skilled | AVI_ocp_semi_skilled | AVI_ocp_low_skilled | AVI_ocp_unskilled | AVI_ocp_Skilled | AVI_ocp_Unskilled | median_wage_15_19 | median_wage_20_24 | median_wage_25_29 | median_wage_30_34 | median_wage_35_39 | median_wage_40_44 | median_wage_45_49 | median_wage_50_54 | median_wage_55_59 | median_wage_60_64 | median_wage_65 | median_wage_Total_All_Ages | GDP_Northland | GDP_Auckland | GDP_Waikato | GDP_Bay_of_Plenty | GDP_Gisborne | GDP_Hawkes_Bay | GDP_Taranaki | GDP_Manawatu_Whanganui | GDP_Wellington | GDP_West_Coast | GDP_Canterbury | GDP_Otago | GDP_Southland | GDP_Marlborough | GDP_Tasman_Nelson | GDP_Total_North_Island | GDP_Total_South_Island | GDP_New_Zealand | GDP_Agriculture | GDP_Forestry__Fishing__and_Mining | GDP_Forestry__Fishing__Mining__Electricity__Gas__Water_and_Waste_Services | GDP_Primary_Manufacturing | GDP_Other_Manufacturing | GDP_Manufacturing | GDP_Electricity__Gas__Water__and_Waste_services | GDP_Construction | GDP_Wholesale_Trade | GDP_Retail_Trade | GDP_Accommodation_and_Food_Services | GDP_Accommodation | GDP_Food_and_beverage_services | GDP_Transport__Postal_and_Warehousing | GDP_Information_Media__Telecommunications_and_Other_Services | GDP_Financial_and_Insurance_Services | GDP_Rental__Hiring_and_Real_Estate_Services | GDP_Owner_Occupied_Property_Operation | GDP_Professional__Scientific__and_Technical_Services | GDP_Administrative_and_Support_Services | GDP_Public_Administration_and_Safety | GDP_Education_and_Training | GDP_Health_Care_and_Social_Assistance | GDP_Total_All_Industries | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2010 | 18130 | 1580 | 3320 | 250 | 830 | 840 | 1240 | 475 | 800 | 2730 | 4910 | 15380 | 1430 | 3470 | 195 | 850 | 980 | 1300 | 515 | 900 | 2270 | 5290 | 645.700012 | 2844.399902 | 77.300003 | 6.3 | 72.400002 | 2198.699951 | 100.000000 | 100.000000 | 100.000000 | 100.000000 | 100.000000 | 100.000000 | 100.000000 | 100.000000 | 100.000000 | 100.000000 | 100.000000 | 100.000000 | 100.000000 | 100.000000 | 100.000000 | 100.000000 | 100.000000 | 100.000000 | 100.000000 | 100.000000 | 100.000000 | 100.000000 | 100.000000 | 100.000000 | 100.000000 | 100.000000 | 100.000000 | 100.000000 | 100.000000 | 100.000000 | 100.000000 | 100.000000 | 100.000000 | 100.000000 | 100.000000 | 3060.0 | 8050.0 | 10582.5 | 11865.0 | 12315.0 | 12137.5 | 12065.0 | 11905.0 | 11437.5 | 10515.0 | 7245.0 | 10632.5 | 4543.0 | 63382.0 | 15258.0 | 9409.0 | 1324.0 | 5150.0 | 7665.0 | 7544.0 | 25113.0 | 1351.0 | 22229.0 | 7894.0 | 4168.0 | 1794.0 | 3296.0 | 139388.0 | 40731.0 | 180119.0 | 8654 | 6651 | 12720 | 13001 | 7925 | 20926 | 6069 | 10464 | 9869 | 8292 | 3789 | 1226 | 2563 | 7929 | 12490 | 11119 | 13413 | 12192 | 14735 | 3633 | 8732 | 9204 | 11958 | 180119 |
| 1 | 2011 | 17310 | 1600 | 4100 | 190 | 710 | 810 | 1140 | 480 | 850 | 2220 | 5310 | 14540 | 1370 | 3730 | 190 | 690 | 890 | 1110 | 490 | 760 | 1920 | 5120 | 655.799988 | 2866.699951 | 77.099998 | 6.4 | 72.199997 | 2211.000000 | 101.537247 | 103.784752 | 131.811493 | 107.877747 | 111.249252 | 104.018753 | 109.501999 | 116.044502 | 105.596497 | 101.322998 | 106.247749 | 115.751999 | 92.198753 | 104.268753 | 107.144249 | 102.214249 | 111.413254 | 123.854248 | 101.984749 | 108.947250 | 103.917252 | 106.695747 | 121.152000 | 115.340752 | 107.325996 | 105.281250 | 113.408501 | 118.313751 | 105.897751 | 103.693748 | 126.957001 | 111.033752 | 104.367249 | 108.664749 | 109.756752 | 3152.5 | 8162.5 | 10835.0 | 12155.0 | 12712.5 | 12565.0 | 12462.5 | 12290.0 | 11845.0 | 10967.5 | 7752.5 | 10972.5 | 4842.0 | 65759.0 | 16075.0 | 9947.0 | 1394.0 | 5447.0 | 7977.0 | 7888.0 | 25501.0 | 1427.0 | 23219.0 | 8171.0 | 4498.0 | 1813.0 | 3446.0 | 144830.0 | 42574.0 | 187404.0 | 10643 | 6938 | 13291 | 13860 | 8191 | 22051 | 6353 | 10470 | 10081 | 8469 | 3857 | 1248 | 2609 | 8758 | 12421 | 9975 | 13648 | 13484 | 15529 | 3959 | 8835 | 9595 | 12337 | 187404 |
| 2 | 2012 | 17260 | 1560 | 4580 | 195 | 820 | 1040 | 1230 | 510 | 800 | 2710 | 5880 | 14410 | 1510 | 3710 | 160 | 920 | 940 | 1200 | 450 | 790 | 2290 | 5080 | 652.500000 | 2876.300049 | 77.300003 | 6.3 | 72.400002 | 2223.800049 | 104.071747 | 110.633003 | 170.327499 | 120.652748 | 129.169006 | 117.805748 | 115.172501 | 138.578751 | 110.881500 | 102.747253 | 109.499001 | 147.468002 | 83.279999 | 119.278000 | 112.457253 | 97.901749 | 125.172997 | 148.839752 | 105.112251 | 113.703247 | 106.715752 | 107.819504 | 142.382004 | 133.338745 | 113.677498 | 107.552750 | 137.171494 | 145.013245 | 107.701500 | 110.109497 | 150.747253 | 120.932503 | 116.904999 | 113.887749 | 120.466751 | 3257.5 | 8370.0 | 11112.5 | 12452.5 | 13102.5 | 13020.0 | 12925.0 | 12735.0 | 12280.0 | 11435.0 | 8245.0 | 11335.0 | 4922.0 | 68978.0 | 17102.0 | 10321.0 | 1432.0 | 5575.0 | 7896.0 | 7997.0 | 26724.0 | 1517.0 | 24316.0 | 8402.0 | 4599.0 | 1919.0 | 3503.0 | 150947.0 | 44256.0 | 195203.0 | 10622 | 6772 | 13371 | 15298 | 8017 | 23315 | 6600 | 10804 | 10526 | 8878 | 3940 | 1245 | 2696 | 9331 | 12960 | 10625 | 14757 | 14344 | 15869 | 4172 | 9041 | 9985 | 12665 | 195203 |
| 3 | 2013 | 18320 | 1800 | 4360 | 305 | 790 | 770 | 1290 | 660 | 830 | 2110 | 5820 | 13340 | 1470 | 3770 | 245 | 710 | 780 | 1070 | 480 | 760 | 1950 | 4720 | 666.900024 | 2878.800049 | 76.800003 | 6.6 | 71.800003 | 2211.899902 | 116.276497 | 130.563995 | 191.616257 | 127.156250 | 136.180756 | 137.263504 | 120.304001 | 150.940750 | 123.468246 | 106.194748 | 112.277748 | 184.800247 | 92.263496 | 123.594498 | 134.522247 | 92.378502 | 153.481247 | 158.919006 | 113.936996 | 122.010750 | 116.591751 | 108.565002 | 171.246750 | 157.548996 | 120.545998 | 118.168999 | 180.684998 | 190.806747 | 110.852997 | 123.290001 | 181.366257 | 137.462250 | 138.647995 | 123.331497 | 138.688507 | 3290.0 | 8555.0 | 11340.0 | 12705.0 | 13407.5 | 13420.0 | 13267.5 | 13065.0 | 12647.5 | 11850.0 | 8620.0 | 11617.5 | 4899.0 | 71351.0 | 16725.0 | 10490.0 | 1427.0 | 5730.0 | 7967.0 | 7999.0 | 27149.0 | 1449.0 | 25371.0 | 8526.0 | 4432.0 | 2001.0 | 3581.0 | 153739.0 | 45360.0 | 199099.0 | 9130 | 6610 | 13764 | 15133 | 7938 | 23071 | 7154 | 11541 | 10530 | 9489 | 4124 | 1270 | 2855 | 9790 | 13188 | 10921 | 15134 | 15260 | 16555 | 4165 | 9147 | 10154 | 13133 | 199099 |
| 4 | 2014 | 18870 | 1580 | 4710 | 285 | 690 | 860 | 1380 | 550 | 1010 | 2350 | 6050 | 15940 | 1620 | 3600 | 245 | 720 | 800 | 1180 | 600 | 690 | 2400 | 4680 | 641.799988 | 2899.399902 | 77.900002 | 6.0 | 73.199997 | 2257.600098 | 133.691757 | 154.134003 | 220.149506 | 143.167496 | 165.084747 | 144.570755 | 141.191498 | 188.934753 | 140.002243 | 111.706253 | 121.781998 | 230.177505 | 106.677002 | 129.583496 | 162.815247 | 95.911003 | 181.682999 | 183.427505 | 128.405243 | 121.794746 | 135.623993 | 115.741753 | 207.521744 | 180.007996 | 132.238754 | 131.477249 | 223.298996 | 249.287994 | 120.819504 | 142.899002 | 209.124252 | 158.863007 | 167.512497 | 138.882507 | 162.422501 | 3392.5 | 8820.0 | 11590.0 | 13025.0 | 13767.5 | 13877.5 | 13655.0 | 13475.0 | 13017.5 | 12245.0 | 9032.5 | 11925.0 | 5363.0 | 75444.0 | 18690.0 | 11030.0 | 1474.0 | 6044.0 | 8508.0 | 8558.0 | 28348.0 | 1548.0 | 28198.0 | 9224.0 | 5075.0 | 2224.0 | 3873.0 | 163457.0 | 50143.0 | 213600.0 | 13111 | 6842 | 14266 | 15619 | 8209 | 23828 | 7424 | 12398 | 11592 | 9987 | 4368 | 1336 | 3034 | 10356 | 13731 | 12265 | 16128 | 16147 | 17763 | 4283 | 9399 | 10503 | 13473 | 213600 |
| 5 | 2015 | 18550 | 1920 | 4540 | 180 | 780 | 720 | 1340 | 485 | 770 | 2440 | 5750 | 16200 | 1390 | 3960 | 205 | 800 | 780 | 1240 | 550 | 970 | 2510 | 4920 | 625.299988 | 2953.699951 | 78.800003 | 5.6 | 74.400002 | 2328.300049 | 145.303253 | 172.999496 | 209.960754 | 155.774750 | 165.059494 | 143.589996 | 150.659256 | 198.530746 | 137.801743 | 107.152000 | 132.479752 | 241.839005 | 129.024750 | 136.664001 | 171.364746 | 84.667503 | 188.766251 | 170.084000 | 129.656998 | 125.584000 | 142.624496 | 117.330002 | 215.301498 | 185.268997 | 139.214996 | 135.110001 | 226.864502 | 258.147491 | 122.263000 | 153.565750 | 220.054993 | 163.960999 | 173.757248 | 142.871506 | 167.606003 | 3470.0 | 9025.0 | 11745.0 | 13300.0 | 14057.5 | 14255.0 | 14037.5 | 13852.5 | 13335.0 | 12595.0 | 9360.0 | 12192.5 | 5555.0 | 81076.0 | 18754.0 | 11291.0 | 1541.0 | 6176.0 | 8329.0 | 8681.0 | 29588.0 | 1479.0 | 29434.0 | 9427.0 | 4841.0 | 2338.0 | 3996.0 | 170990.0 | 51516.0 | 222506.0 | 8240 | 6936 | 14531 | 18113 | 8753 | 26866 | 7595 | 13833 | 12486 | 10491 | 4763 | 1364 | 3420 | 11424 | 14452 | 13514 | 16526 | 17148 | 18570 | 4556 | 9824 | 10998 | 14263 | 222506 |
| 6 | 2016 | 18440 | 2120 | 5510 | 285 | 730 | 1000 | 1560 | 585 | 830 | 2820 | 5990 | 14920 | 2220 | 4270 | 285 | 700 | 800 | 1270 | 600 | 880 | 2130 | 5350 | 646.799988 | 3022.500000 | 78.599998 | 5.6 | 74.199997 | 2375.800049 | 165.826004 | 216.454498 | 201.011993 | 185.867996 | 197.029999 | 155.698746 | 170.623505 | 227.022003 | 159.732498 | 118.360252 | 147.285751 | 278.177765 | 139.579498 | 144.005997 | 196.753754 | 84.843498 | 219.529999 | 196.574997 | 141.570251 | 154.535995 | 158.963257 | 124.945999 | 250.199753 | 201.402496 | 156.169006 | 144.600494 | 269.366760 | 312.987000 | 131.723007 | 172.766006 | 252.503250 | 181.384995 | 202.331497 | 157.087494 | 188.636505 | 3570.0 | 9267.5 | 11977.5 | 13605.0 | 14405.0 | 14702.5 | 14470.0 | 14267.5 | 13745.0 | 12967.5 | 9682.5 | 12505.0 | 5994.0 | 88139.0 | 19413.0 | 12287.0 | 1638.0 | 6444.0 | 7221.0 | 8944.0 | 30654.0 | 1380.0 | 30338.0 | 10057.0 | 4693.0 | 2440.0 | 4216.0 | 180735.0 | 53124.0 | 233859.0 | 7691 | 6107 | 13779 | 19703 | 9105 | 28808 | 7673 | 15170 | 12856 | 11083 | 5327 | 1560 | 3745 | 12430 | 15012 | 13962 | 17919 | 18106 | 19932 | 4889 | 10322 | 11431 | 15164 | 233859 |
| 7 | 2017 | 22050 | 2090 | 5140 | 265 | 950 | 1120 | 1640 | 655 | 910 | 2640 | 8000 | 15250 | 1710 | 4870 | 260 | 710 | 860 | 1200 | 590 | 630 | 2260 | 6650 | 608.700012 | 3103.300049 | 80.400002 | 5.3 | 76.099998 | 2494.699951 | 175.229996 | 243.526505 | 211.830994 | 214.937744 | 250.458755 | 179.497757 | 211.601257 | 282.455750 | 195.681747 | 127.823753 | 148.992752 | 310.851257 | 149.963501 | 158.511505 | 214.954742 | 77.326752 | 279.994263 | 241.283005 | 147.703995 | 228.611252 | 170.497757 | 130.923248 | 269.986237 | 214.666000 | 163.593002 | 159.660248 | 348.409241 | 390.851257 | 139.533249 | 182.877243 | 272.595490 | 207.358246 | 232.789993 | 166.677505 | 216.222748 | 3822.5 | 9607.5 | 12285.0 | 13912.5 | 14750.0 | 15095.0 | 14952.5 | 14665.0 | 14147.5 | 13327.5 | 9995.0 | 12832.5 | 6428.0 | 93624.0 | 20709.0 | 13820.0 | 1707.0 | 7011.0 | 7279.0 | 9358.0 | 32240.0 | 1450.0 | 31189.0 | 10745.0 | 5122.0 | 2604.0 | 4492.0 | 192176.0 | 55602.0 | 247778.0 | 11108 | 6438 | 14320 | 17562 | 9820 | 27381 | 7882 | 16763 | 12843 | 12294 | 5947 | 1830 | 4213 | 12263 | 15447 | 14487 | 19690 | 19297 | 21780 | 5330 | 10902 | 11924 | 15905 | 247778 |
| 8 | 2018 | 22660 | 2360 | 6720 | 290 | 860 | 970 | 1720 | 590 | 880 | 2940 | 7220 | 17380 | 2060 | 5530 | 217 | 730 | 1150 | 1560 | 485 | 770 | 2680 | 6270 | 600.799988 | 3165.199951 | 81.000000 | 4.8 | 77.099998 | 2564.300049 | 178.728500 | 283.244751 | 211.246750 | 258.770752 | 284.169250 | 210.156754 | 243.121506 | 327.308746 | 224.371002 | 139.305252 | 156.547256 | 319.922485 | 155.553253 | 189.209747 | 231.892502 | 86.007004 | 302.975006 | 271.968262 | 152.390747 | 249.154999 | 177.337997 | 140.409256 | 275.519501 | 234.194504 | 171.591003 | 166.481247 | 376.268005 | 453.763000 | 150.222244 | 187.268250 | 277.112244 | 222.082993 | 257.696014 | 175.389252 | 234.239502 | 4052.5 | 10072.5 | 12815.0 | 14397.5 | 15257.5 | 15665.0 | 15582.5 | 15247.5 | 14727.5 | 13915.0 | 10507.5 | 13357.5 | 6970.0 | 99794.0 | 22583.0 | 15106.0 | 1844.0 | 7567.0 | 8129.0 | 10123.0 | 33610.0 | 1632.0 | 33555.0 | 11754.0 | 5609.0 | 2798.0 | 4835.0 | 205727.0 | 60184.0 | 265911.0 | 12610 | 7422 | 15426 | 19546 | 10092 | 29638 | 8004 | 19009 | 13977 | 12896 | 6247 | 1922 | 4440 | 12909 | 15889 | 16262 | 20538 | 20772 | 23382 | 5728 | 11531 | 12275 | 16708 | 265911 |
| 9 | 2019 | 19170 | 2290 | 5700 | 270 | 900 | 980 | 1820 | 725 | 1010 | 3260 | 6840 | 20200 | 2130 | 4860 | 225 | 920 | 980 | 1630 | 690 | 920 | 2600 | 6690 | 608.700012 | 3219.500000 | 81.099998 | 4.5 | 77.500000 | 2610.899902 | 170.895752 | 290.044250 | 197.054245 | 286.093994 | 286.961487 | 212.753998 | 235.375000 | 340.894012 | 224.387756 | 148.065750 | 152.729004 | 310.461243 | 152.830002 | 194.334244 | 226.292252 | 91.447746 | 286.351746 | 268.006012 | 147.673248 | 282.870514 | 173.587006 | 141.817245 | 267.490753 | 244.368256 | 169.208496 | 154.995743 | 349.331238 | 473.024994 | 150.126999 | 187.576508 | 273.232513 | 209.394745 | 263.279999 | 176.359497 | 226.138748 | 4252.5 | 10460.0 | 13295.0 | 14860.0 | 15780.0 | 16160.0 | 16220.0 | 15822.5 | 15237.5 | 14417.5 | 11020.0 | 13852.5 | 7238.0 | 105433.0 | 24349.0 | 16322.0 | 1999.0 | 7886.0 | 8245.0 | 10744.0 | 34849.0 | 1685.0 | 35066.0 | 12484.0 | 5932.0 | 2966.0 | 5162.0 | 217065.0 | 63295.0 | 280360.0 | 12655 | 7652 | 16141 | 19888 | 10567 | 30455 | 8489 | 20314 | 14394 | 13503 | 6471 | 2105 | 4609 | 13609 | 16734 | 17161 | 21988 | 22127 | 25041 | 6116 | 12279 | 13099 | 18030 | 280360 |
| 10 | 2020 | 20070 | 1950 | 5130 | 250 | 940 | 990 | 1470 | 680 | 1090 | 2800 | 6460 | 17190 | 1740 | 4740 | 175 | 750 | 1010 | 1410 | 570 | 1000 | 2400 | 5170 | 622.200012 | 3272.399902 | 81.000000 | 4.3 | 77.500000 | 2650.300049 | 146.184753 | 271.098236 | 167.698502 | 316.912262 | 272.200256 | 218.504745 | 242.593246 | 294.454254 | 207.248245 | 139.345505 | 134.226257 | 288.206512 | 141.802505 | 193.320496 | 193.007751 | 77.951500 | 267.991241 | 261.999756 | 123.960999 | 272.392761 | 156.774246 | 128.820496 | 247.310501 | 216.382996 | 132.521744 | 130.047745 | 342.761261 | 454.219513 | 136.427994 | 165.027496 | 256.376495 | 181.031250 | 236.547256 | 154.667007 | 199.125748 | 4595.0 | 10627.5 | 13565.0 | 15062.5 | 15942.5 | 16417.5 | 16535.0 | 16147.5 | 15552.5 | 14747.5 | 11495.0 | 14167.5 | 7712.0 | 110312.0 | 25993.0 | 17283.0 | 2056.0 | 8397.0 | 8483.0 | 11344.0 | 37561.0 | 1725.0 | 37099.0 | 13097.0 | 6093.0 | 3135.0 | 5487.0 | 229139.0 | 66638.0 | 295777.0 | 13866 | 7383 | 16375 | 20524 | 10733 | 31257 | 8992 | 22400 | 14558 | 13983 | 6823 | 2140 | 4875 | 13817 | 17359 | 17594 | 23654 | 23810 | 27100 | 6289 | 13457 | 13795 | 19447 | 295777 |
| 11 | 2021 | 22980 | 3300 | 6180 | 270 | 1390 | 1090 | 1780 | 790 | 1160 | 3800 | 7750 | 16450 | 2340 | 4950 | 185 | 770 | 930 | 1390 | 660 | 860 | 2800 | 5470 | 643.700012 | 3318.600098 | 80.599998 | 4.9 | 76.599998 | 2674.899902 | 189.515747 | 392.755005 | 248.804749 | 451.379486 | 381.414490 | 306.777740 | 344.608765 | 396.520996 | 291.072754 | 195.253494 | 177.007507 | 389.319000 | 197.629745 | 294.096985 | 233.626251 | 115.649002 | 401.498993 | 338.093994 | 157.812500 | 369.927002 | 191.849243 | 180.051498 | 329.804504 | 282.026245 | 194.417496 | 180.381500 | 487.175751 | 639.705994 | 183.621002 | 209.551743 | 336.203491 | 257.895996 | 337.497498 | 211.006256 | 282.832764 | 4875.0 | 11410.0 | 14522.5 | 16010.0 | 16915.0 | 17427.5 | 17552.5 | 17115.0 | 16440.0 | 15597.5 | 12175.0 | 15062.5 | 7804.0 | 113290.0 | 26954.0 | 18012.0 | 2256.0 | 8878.0 | 8077.0 | 12115.0 | 37606.0 | 1784.0 | 37011.0 | 12860.0 | 6180.0 | 3197.0 | 5582.0 | 234991.0 | 66613.0 | 301604.0 | 14319 | 6645 | 15200 | 19432 | 11376 | 30808 | 8555 | 23294 | 15958 | 15235 | 6002 | 1557 | 4255 | 11724 | 17244 | 18121 | 24686 | 25464 | 28022 | 5633 | 14219 | 14507 | 21356 | 301604 |
| 12 | 2022 | 23740 | 2460 | 5800 | 265 | 940 | 1130 | 1870 | 620 | 1160 | 3500 | 6770 | 17440 | 2070 | 4100 | 345 | 770 | 890 | 1510 | 570 | 1110 | 2720 | 5670 | 603.099976 | 3310.199951 | 81.800003 | 3.6 | 78.800003 | 2707.100098 | 200.328506 | 429.891235 | 286.851013 | 459.618011 | 394.096741 | 342.017487 | 402.063995 | 457.423737 | 302.150757 | 192.752747 | 188.985748 | 391.059265 | 213.925003 | 333.005493 | 338.508240 | 107.830002 | 437.896240 | 342.504486 | 173.082993 | 368.586761 | 188.493744 | 186.207748 | 348.890259 | 362.155762 | 208.888000 | 201.230743 | 492.319244 | 695.994019 | 185.337250 | 241.535507 | 342.635986 | 281.004242 | 386.713257 | 217.796249 | 314.244263 | 5285.0 | 12522.5 | 15717.5 | 17320.0 | 18235.0 | 18725.0 | 18897.5 | 18440.0 | 17642.5 | 16700.0 | 13062.5 | 16185.0 | 8684.0 | 124270.0 | 29378.0 | 19527.0 | 2318.0 | 9773.0 | 8911.0 | 13110.0 | 40777.0 | 1798.0 | 40380.0 | 14016.0 | 7144.0 | 3283.0 | 6038.0 | 256748.0 | 72659.0 | 329407.0 | 16373 | 6805 | 16463 | 19567 | 12030 | 31597 | 9658 | 24320 | 18992 | 16985 | 6851 | 1743 | 4896 | 13124 | 18604 | 20252 | 26033 | 27240 | 31774 | 6671 | 15185 | 15489 | 23665 | 329407 |
unemployment_rate_label = np.where(merged_data['Unemployment_Rate'] > 5.6, 'High', 'Low')
avi_auckland_label = np.where(merged_data['AVI_Auckland'] > 146.18475, 'High', 'Low')
new_columns = pd.DataFrame({'Unemployment Rate Classified': unemployment_rate_label,'AVI Auckland Classified': avi_auckland_label})
merged_data = pd.concat([merged_data, new_columns], axis=1)
merged_data1 = merged_data.copy()
merged_data1
| Year | job_creation_Auckland | job_creation_Bay_of_Plenty | job_creation_Canterbury | job_creation_Gisborne | job_creation_Hawkes_Bay | job_creation_Manawatu_Wanganui | job_creation_Otago | job_creation_Taranaki | job_creation_Tasman__Nelson__Marlborough__West_Coast | job_creation_Waikato | job_creation_Wellington | job_destruction_Auckland | job_destruction_Bay_of_Plenty | job_destruction_Canterbury | job_destruction_Gisborne | job_destruction_Hawkes_Bay | job_destruction_Manawatu_Wanganui | job_destruction_Otago | job_destruction_Taranaki | job_destruction_Tasman__Nelson__Marlborough__West_Coast | job_destruction_Waikato | job_destruction_Wellington | Not_in_Labour_Force | Working_Age_Population | Labour_Force_Participation_Rate | Unemployment_Rate | Employment_Rate | Total_Labour_Force | AVI_Auckland | AVI_Bay_of_Plenty | AVI_Canterbury | AVI_Gisborne_Hawkes_Bay | AVI_Marlborough_NelsonTasman_West_Coast | AVI_Manawatu_Wanganui_Taranaki | AVI_Northland | AVI_Otago_Southland | AVI_Waikato | AVI_Wellington | AVI_ids_Accounting | AVI_ids_Construction | AVI_ids_Education | AVI_ids_Health | AVI_ids_Hospitalty | AVI_ids_IT | AVI_ids_Manufacturing | AVI_ids_Primary | AVI_ids_Sales | AVI_ids_Other | AVI_ocp_Managers | AVI_ocp_Professionals | AVI_ocp_Tech_and_Trades | AVI_ocp_Community_and_Personal_Services | AVI_ocp_Clerical_and_Administration | AVI_ocp_Sales | AVI_ocp_Machinery_Drivers | AVI_ocp_Labourers | AVI_ocp_highly_skilled | AVI_ocp_skilled | AVI_ocp_semi_skilled | AVI_ocp_low_skilled | AVI_ocp_unskilled | AVI_ocp_Skilled | AVI_ocp_Unskilled | median_wage_15_19 | median_wage_20_24 | median_wage_25_29 | median_wage_30_34 | median_wage_35_39 | median_wage_40_44 | median_wage_45_49 | median_wage_50_54 | median_wage_55_59 | median_wage_60_64 | median_wage_65 | median_wage_Total_All_Ages | GDP_Northland | GDP_Auckland | GDP_Waikato | GDP_Bay_of_Plenty | GDP_Gisborne | GDP_Hawkes_Bay | GDP_Taranaki | GDP_Manawatu_Whanganui | GDP_Wellington | GDP_West_Coast | GDP_Canterbury | GDP_Otago | GDP_Southland | GDP_Marlborough | GDP_Tasman_Nelson | GDP_Total_North_Island | GDP_Total_South_Island | GDP_New_Zealand | GDP_Agriculture | GDP_Forestry__Fishing__and_Mining | GDP_Forestry__Fishing__Mining__Electricity__Gas__Water_and_Waste_Services | GDP_Primary_Manufacturing | GDP_Other_Manufacturing | GDP_Manufacturing | GDP_Electricity__Gas__Water__and_Waste_services | GDP_Construction | GDP_Wholesale_Trade | GDP_Retail_Trade | GDP_Accommodation_and_Food_Services | GDP_Accommodation | GDP_Food_and_beverage_services | GDP_Transport__Postal_and_Warehousing | GDP_Information_Media__Telecommunications_and_Other_Services | GDP_Financial_and_Insurance_Services | GDP_Rental__Hiring_and_Real_Estate_Services | GDP_Owner_Occupied_Property_Operation | GDP_Professional__Scientific__and_Technical_Services | GDP_Administrative_and_Support_Services | GDP_Public_Administration_and_Safety | GDP_Education_and_Training | GDP_Health_Care_and_Social_Assistance | GDP_Total_All_Industries | Unemployment Rate Classified | AVI Auckland Classified | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2010 | 18130 | 1580 | 3320 | 250 | 830 | 840 | 1240 | 475 | 800 | 2730 | 4910 | 15380 | 1430 | 3470 | 195 | 850 | 980 | 1300 | 515 | 900 | 2270 | 5290 | 645.700012 | 2844.399902 | 77.300003 | 6.3 | 72.400002 | 2198.699951 | 100.000000 | 100.000000 | 100.000000 | 100.000000 | 100.000000 | 100.000000 | 100.000000 | 100.000000 | 100.000000 | 100.000000 | 100.000000 | 100.000000 | 100.000000 | 100.000000 | 100.000000 | 100.000000 | 100.000000 | 100.000000 | 100.000000 | 100.000000 | 100.000000 | 100.000000 | 100.000000 | 100.000000 | 100.000000 | 100.000000 | 100.000000 | 100.000000 | 100.000000 | 100.000000 | 100.000000 | 100.000000 | 100.000000 | 100.000000 | 100.000000 | 3060.0 | 8050.0 | 10582.5 | 11865.0 | 12315.0 | 12137.5 | 12065.0 | 11905.0 | 11437.5 | 10515.0 | 7245.0 | 10632.5 | 4543.0 | 63382.0 | 15258.0 | 9409.0 | 1324.0 | 5150.0 | 7665.0 | 7544.0 | 25113.0 | 1351.0 | 22229.0 | 7894.0 | 4168.0 | 1794.0 | 3296.0 | 139388.0 | 40731.0 | 180119.0 | 8654 | 6651 | 12720 | 13001 | 7925 | 20926 | 6069 | 10464 | 9869 | 8292 | 3789 | 1226 | 2563 | 7929 | 12490 | 11119 | 13413 | 12192 | 14735 | 3633 | 8732 | 9204 | 11958 | 180119 | High | Low |
| 1 | 2011 | 17310 | 1600 | 4100 | 190 | 710 | 810 | 1140 | 480 | 850 | 2220 | 5310 | 14540 | 1370 | 3730 | 190 | 690 | 890 | 1110 | 490 | 760 | 1920 | 5120 | 655.799988 | 2866.699951 | 77.099998 | 6.4 | 72.199997 | 2211.000000 | 101.537247 | 103.784752 | 131.811493 | 107.877747 | 111.249252 | 104.018753 | 109.501999 | 116.044502 | 105.596497 | 101.322998 | 106.247749 | 115.751999 | 92.198753 | 104.268753 | 107.144249 | 102.214249 | 111.413254 | 123.854248 | 101.984749 | 108.947250 | 103.917252 | 106.695747 | 121.152000 | 115.340752 | 107.325996 | 105.281250 | 113.408501 | 118.313751 | 105.897751 | 103.693748 | 126.957001 | 111.033752 | 104.367249 | 108.664749 | 109.756752 | 3152.5 | 8162.5 | 10835.0 | 12155.0 | 12712.5 | 12565.0 | 12462.5 | 12290.0 | 11845.0 | 10967.5 | 7752.5 | 10972.5 | 4842.0 | 65759.0 | 16075.0 | 9947.0 | 1394.0 | 5447.0 | 7977.0 | 7888.0 | 25501.0 | 1427.0 | 23219.0 | 8171.0 | 4498.0 | 1813.0 | 3446.0 | 144830.0 | 42574.0 | 187404.0 | 10643 | 6938 | 13291 | 13860 | 8191 | 22051 | 6353 | 10470 | 10081 | 8469 | 3857 | 1248 | 2609 | 8758 | 12421 | 9975 | 13648 | 13484 | 15529 | 3959 | 8835 | 9595 | 12337 | 187404 | High | Low |
| 2 | 2012 | 17260 | 1560 | 4580 | 195 | 820 | 1040 | 1230 | 510 | 800 | 2710 | 5880 | 14410 | 1510 | 3710 | 160 | 920 | 940 | 1200 | 450 | 790 | 2290 | 5080 | 652.500000 | 2876.300049 | 77.300003 | 6.3 | 72.400002 | 2223.800049 | 104.071747 | 110.633003 | 170.327499 | 120.652748 | 129.169006 | 117.805748 | 115.172501 | 138.578751 | 110.881500 | 102.747253 | 109.499001 | 147.468002 | 83.279999 | 119.278000 | 112.457253 | 97.901749 | 125.172997 | 148.839752 | 105.112251 | 113.703247 | 106.715752 | 107.819504 | 142.382004 | 133.338745 | 113.677498 | 107.552750 | 137.171494 | 145.013245 | 107.701500 | 110.109497 | 150.747253 | 120.932503 | 116.904999 | 113.887749 | 120.466751 | 3257.5 | 8370.0 | 11112.5 | 12452.5 | 13102.5 | 13020.0 | 12925.0 | 12735.0 | 12280.0 | 11435.0 | 8245.0 | 11335.0 | 4922.0 | 68978.0 | 17102.0 | 10321.0 | 1432.0 | 5575.0 | 7896.0 | 7997.0 | 26724.0 | 1517.0 | 24316.0 | 8402.0 | 4599.0 | 1919.0 | 3503.0 | 150947.0 | 44256.0 | 195203.0 | 10622 | 6772 | 13371 | 15298 | 8017 | 23315 | 6600 | 10804 | 10526 | 8878 | 3940 | 1245 | 2696 | 9331 | 12960 | 10625 | 14757 | 14344 | 15869 | 4172 | 9041 | 9985 | 12665 | 195203 | High | Low |
| 3 | 2013 | 18320 | 1800 | 4360 | 305 | 790 | 770 | 1290 | 660 | 830 | 2110 | 5820 | 13340 | 1470 | 3770 | 245 | 710 | 780 | 1070 | 480 | 760 | 1950 | 4720 | 666.900024 | 2878.800049 | 76.800003 | 6.6 | 71.800003 | 2211.899902 | 116.276497 | 130.563995 | 191.616257 | 127.156250 | 136.180756 | 137.263504 | 120.304001 | 150.940750 | 123.468246 | 106.194748 | 112.277748 | 184.800247 | 92.263496 | 123.594498 | 134.522247 | 92.378502 | 153.481247 | 158.919006 | 113.936996 | 122.010750 | 116.591751 | 108.565002 | 171.246750 | 157.548996 | 120.545998 | 118.168999 | 180.684998 | 190.806747 | 110.852997 | 123.290001 | 181.366257 | 137.462250 | 138.647995 | 123.331497 | 138.688507 | 3290.0 | 8555.0 | 11340.0 | 12705.0 | 13407.5 | 13420.0 | 13267.5 | 13065.0 | 12647.5 | 11850.0 | 8620.0 | 11617.5 | 4899.0 | 71351.0 | 16725.0 | 10490.0 | 1427.0 | 5730.0 | 7967.0 | 7999.0 | 27149.0 | 1449.0 | 25371.0 | 8526.0 | 4432.0 | 2001.0 | 3581.0 | 153739.0 | 45360.0 | 199099.0 | 9130 | 6610 | 13764 | 15133 | 7938 | 23071 | 7154 | 11541 | 10530 | 9489 | 4124 | 1270 | 2855 | 9790 | 13188 | 10921 | 15134 | 15260 | 16555 | 4165 | 9147 | 10154 | 13133 | 199099 | High | Low |
| 4 | 2014 | 18870 | 1580 | 4710 | 285 | 690 | 860 | 1380 | 550 | 1010 | 2350 | 6050 | 15940 | 1620 | 3600 | 245 | 720 | 800 | 1180 | 600 | 690 | 2400 | 4680 | 641.799988 | 2899.399902 | 77.900002 | 6.0 | 73.199997 | 2257.600098 | 133.691757 | 154.134003 | 220.149506 | 143.167496 | 165.084747 | 144.570755 | 141.191498 | 188.934753 | 140.002243 | 111.706253 | 121.781998 | 230.177505 | 106.677002 | 129.583496 | 162.815247 | 95.911003 | 181.682999 | 183.427505 | 128.405243 | 121.794746 | 135.623993 | 115.741753 | 207.521744 | 180.007996 | 132.238754 | 131.477249 | 223.298996 | 249.287994 | 120.819504 | 142.899002 | 209.124252 | 158.863007 | 167.512497 | 138.882507 | 162.422501 | 3392.5 | 8820.0 | 11590.0 | 13025.0 | 13767.5 | 13877.5 | 13655.0 | 13475.0 | 13017.5 | 12245.0 | 9032.5 | 11925.0 | 5363.0 | 75444.0 | 18690.0 | 11030.0 | 1474.0 | 6044.0 | 8508.0 | 8558.0 | 28348.0 | 1548.0 | 28198.0 | 9224.0 | 5075.0 | 2224.0 | 3873.0 | 163457.0 | 50143.0 | 213600.0 | 13111 | 6842 | 14266 | 15619 | 8209 | 23828 | 7424 | 12398 | 11592 | 9987 | 4368 | 1336 | 3034 | 10356 | 13731 | 12265 | 16128 | 16147 | 17763 | 4283 | 9399 | 10503 | 13473 | 213600 | High | Low |
| 5 | 2015 | 18550 | 1920 | 4540 | 180 | 780 | 720 | 1340 | 485 | 770 | 2440 | 5750 | 16200 | 1390 | 3960 | 205 | 800 | 780 | 1240 | 550 | 970 | 2510 | 4920 | 625.299988 | 2953.699951 | 78.800003 | 5.6 | 74.400002 | 2328.300049 | 145.303253 | 172.999496 | 209.960754 | 155.774750 | 165.059494 | 143.589996 | 150.659256 | 198.530746 | 137.801743 | 107.152000 | 132.479752 | 241.839005 | 129.024750 | 136.664001 | 171.364746 | 84.667503 | 188.766251 | 170.084000 | 129.656998 | 125.584000 | 142.624496 | 117.330002 | 215.301498 | 185.268997 | 139.214996 | 135.110001 | 226.864502 | 258.147491 | 122.263000 | 153.565750 | 220.054993 | 163.960999 | 173.757248 | 142.871506 | 167.606003 | 3470.0 | 9025.0 | 11745.0 | 13300.0 | 14057.5 | 14255.0 | 14037.5 | 13852.5 | 13335.0 | 12595.0 | 9360.0 | 12192.5 | 5555.0 | 81076.0 | 18754.0 | 11291.0 | 1541.0 | 6176.0 | 8329.0 | 8681.0 | 29588.0 | 1479.0 | 29434.0 | 9427.0 | 4841.0 | 2338.0 | 3996.0 | 170990.0 | 51516.0 | 222506.0 | 8240 | 6936 | 14531 | 18113 | 8753 | 26866 | 7595 | 13833 | 12486 | 10491 | 4763 | 1364 | 3420 | 11424 | 14452 | 13514 | 16526 | 17148 | 18570 | 4556 | 9824 | 10998 | 14263 | 222506 | Low | Low |
| 6 | 2016 | 18440 | 2120 | 5510 | 285 | 730 | 1000 | 1560 | 585 | 830 | 2820 | 5990 | 14920 | 2220 | 4270 | 285 | 700 | 800 | 1270 | 600 | 880 | 2130 | 5350 | 646.799988 | 3022.500000 | 78.599998 | 5.6 | 74.199997 | 2375.800049 | 165.826004 | 216.454498 | 201.011993 | 185.867996 | 197.029999 | 155.698746 | 170.623505 | 227.022003 | 159.732498 | 118.360252 | 147.285751 | 278.177765 | 139.579498 | 144.005997 | 196.753754 | 84.843498 | 219.529999 | 196.574997 | 141.570251 | 154.535995 | 158.963257 | 124.945999 | 250.199753 | 201.402496 | 156.169006 | 144.600494 | 269.366760 | 312.987000 | 131.723007 | 172.766006 | 252.503250 | 181.384995 | 202.331497 | 157.087494 | 188.636505 | 3570.0 | 9267.5 | 11977.5 | 13605.0 | 14405.0 | 14702.5 | 14470.0 | 14267.5 | 13745.0 | 12967.5 | 9682.5 | 12505.0 | 5994.0 | 88139.0 | 19413.0 | 12287.0 | 1638.0 | 6444.0 | 7221.0 | 8944.0 | 30654.0 | 1380.0 | 30338.0 | 10057.0 | 4693.0 | 2440.0 | 4216.0 | 180735.0 | 53124.0 | 233859.0 | 7691 | 6107 | 13779 | 19703 | 9105 | 28808 | 7673 | 15170 | 12856 | 11083 | 5327 | 1560 | 3745 | 12430 | 15012 | 13962 | 17919 | 18106 | 19932 | 4889 | 10322 | 11431 | 15164 | 233859 | Low | High |
| 7 | 2017 | 22050 | 2090 | 5140 | 265 | 950 | 1120 | 1640 | 655 | 910 | 2640 | 8000 | 15250 | 1710 | 4870 | 260 | 710 | 860 | 1200 | 590 | 630 | 2260 | 6650 | 608.700012 | 3103.300049 | 80.400002 | 5.3 | 76.099998 | 2494.699951 | 175.229996 | 243.526505 | 211.830994 | 214.937744 | 250.458755 | 179.497757 | 211.601257 | 282.455750 | 195.681747 | 127.823753 | 148.992752 | 310.851257 | 149.963501 | 158.511505 | 214.954742 | 77.326752 | 279.994263 | 241.283005 | 147.703995 | 228.611252 | 170.497757 | 130.923248 | 269.986237 | 214.666000 | 163.593002 | 159.660248 | 348.409241 | 390.851257 | 139.533249 | 182.877243 | 272.595490 | 207.358246 | 232.789993 | 166.677505 | 216.222748 | 3822.5 | 9607.5 | 12285.0 | 13912.5 | 14750.0 | 15095.0 | 14952.5 | 14665.0 | 14147.5 | 13327.5 | 9995.0 | 12832.5 | 6428.0 | 93624.0 | 20709.0 | 13820.0 | 1707.0 | 7011.0 | 7279.0 | 9358.0 | 32240.0 | 1450.0 | 31189.0 | 10745.0 | 5122.0 | 2604.0 | 4492.0 | 192176.0 | 55602.0 | 247778.0 | 11108 | 6438 | 14320 | 17562 | 9820 | 27381 | 7882 | 16763 | 12843 | 12294 | 5947 | 1830 | 4213 | 12263 | 15447 | 14487 | 19690 | 19297 | 21780 | 5330 | 10902 | 11924 | 15905 | 247778 | Low | High |
| 8 | 2018 | 22660 | 2360 | 6720 | 290 | 860 | 970 | 1720 | 590 | 880 | 2940 | 7220 | 17380 | 2060 | 5530 | 217 | 730 | 1150 | 1560 | 485 | 770 | 2680 | 6270 | 600.799988 | 3165.199951 | 81.000000 | 4.8 | 77.099998 | 2564.300049 | 178.728500 | 283.244751 | 211.246750 | 258.770752 | 284.169250 | 210.156754 | 243.121506 | 327.308746 | 224.371002 | 139.305252 | 156.547256 | 319.922485 | 155.553253 | 189.209747 | 231.892502 | 86.007004 | 302.975006 | 271.968262 | 152.390747 | 249.154999 | 177.337997 | 140.409256 | 275.519501 | 234.194504 | 171.591003 | 166.481247 | 376.268005 | 453.763000 | 150.222244 | 187.268250 | 277.112244 | 222.082993 | 257.696014 | 175.389252 | 234.239502 | 4052.5 | 10072.5 | 12815.0 | 14397.5 | 15257.5 | 15665.0 | 15582.5 | 15247.5 | 14727.5 | 13915.0 | 10507.5 | 13357.5 | 6970.0 | 99794.0 | 22583.0 | 15106.0 | 1844.0 | 7567.0 | 8129.0 | 10123.0 | 33610.0 | 1632.0 | 33555.0 | 11754.0 | 5609.0 | 2798.0 | 4835.0 | 205727.0 | 60184.0 | 265911.0 | 12610 | 7422 | 15426 | 19546 | 10092 | 29638 | 8004 | 19009 | 13977 | 12896 | 6247 | 1922 | 4440 | 12909 | 15889 | 16262 | 20538 | 20772 | 23382 | 5728 | 11531 | 12275 | 16708 | 265911 | Low | High |
| 9 | 2019 | 19170 | 2290 | 5700 | 270 | 900 | 980 | 1820 | 725 | 1010 | 3260 | 6840 | 20200 | 2130 | 4860 | 225 | 920 | 980 | 1630 | 690 | 920 | 2600 | 6690 | 608.700012 | 3219.500000 | 81.099998 | 4.5 | 77.500000 | 2610.899902 | 170.895752 | 290.044250 | 197.054245 | 286.093994 | 286.961487 | 212.753998 | 235.375000 | 340.894012 | 224.387756 | 148.065750 | 152.729004 | 310.461243 | 152.830002 | 194.334244 | 226.292252 | 91.447746 | 286.351746 | 268.006012 | 147.673248 | 282.870514 | 173.587006 | 141.817245 | 267.490753 | 244.368256 | 169.208496 | 154.995743 | 349.331238 | 473.024994 | 150.126999 | 187.576508 | 273.232513 | 209.394745 | 263.279999 | 176.359497 | 226.138748 | 4252.5 | 10460.0 | 13295.0 | 14860.0 | 15780.0 | 16160.0 | 16220.0 | 15822.5 | 15237.5 | 14417.5 | 11020.0 | 13852.5 | 7238.0 | 105433.0 | 24349.0 | 16322.0 | 1999.0 | 7886.0 | 8245.0 | 10744.0 | 34849.0 | 1685.0 | 35066.0 | 12484.0 | 5932.0 | 2966.0 | 5162.0 | 217065.0 | 63295.0 | 280360.0 | 12655 | 7652 | 16141 | 19888 | 10567 | 30455 | 8489 | 20314 | 14394 | 13503 | 6471 | 2105 | 4609 | 13609 | 16734 | 17161 | 21988 | 22127 | 25041 | 6116 | 12279 | 13099 | 18030 | 280360 | Low | High |
| 10 | 2020 | 20070 | 1950 | 5130 | 250 | 940 | 990 | 1470 | 680 | 1090 | 2800 | 6460 | 17190 | 1740 | 4740 | 175 | 750 | 1010 | 1410 | 570 | 1000 | 2400 | 5170 | 622.200012 | 3272.399902 | 81.000000 | 4.3 | 77.500000 | 2650.300049 | 146.184753 | 271.098236 | 167.698502 | 316.912262 | 272.200256 | 218.504745 | 242.593246 | 294.454254 | 207.248245 | 139.345505 | 134.226257 | 288.206512 | 141.802505 | 193.320496 | 193.007751 | 77.951500 | 267.991241 | 261.999756 | 123.960999 | 272.392761 | 156.774246 | 128.820496 | 247.310501 | 216.382996 | 132.521744 | 130.047745 | 342.761261 | 454.219513 | 136.427994 | 165.027496 | 256.376495 | 181.031250 | 236.547256 | 154.667007 | 199.125748 | 4595.0 | 10627.5 | 13565.0 | 15062.5 | 15942.5 | 16417.5 | 16535.0 | 16147.5 | 15552.5 | 14747.5 | 11495.0 | 14167.5 | 7712.0 | 110312.0 | 25993.0 | 17283.0 | 2056.0 | 8397.0 | 8483.0 | 11344.0 | 37561.0 | 1725.0 | 37099.0 | 13097.0 | 6093.0 | 3135.0 | 5487.0 | 229139.0 | 66638.0 | 295777.0 | 13866 | 7383 | 16375 | 20524 | 10733 | 31257 | 8992 | 22400 | 14558 | 13983 | 6823 | 2140 | 4875 | 13817 | 17359 | 17594 | 23654 | 23810 | 27100 | 6289 | 13457 | 13795 | 19447 | 295777 | Low | Low |
| 11 | 2021 | 22980 | 3300 | 6180 | 270 | 1390 | 1090 | 1780 | 790 | 1160 | 3800 | 7750 | 16450 | 2340 | 4950 | 185 | 770 | 930 | 1390 | 660 | 860 | 2800 | 5470 | 643.700012 | 3318.600098 | 80.599998 | 4.9 | 76.599998 | 2674.899902 | 189.515747 | 392.755005 | 248.804749 | 451.379486 | 381.414490 | 306.777740 | 344.608765 | 396.520996 | 291.072754 | 195.253494 | 177.007507 | 389.319000 | 197.629745 | 294.096985 | 233.626251 | 115.649002 | 401.498993 | 338.093994 | 157.812500 | 369.927002 | 191.849243 | 180.051498 | 329.804504 | 282.026245 | 194.417496 | 180.381500 | 487.175751 | 639.705994 | 183.621002 | 209.551743 | 336.203491 | 257.895996 | 337.497498 | 211.006256 | 282.832764 | 4875.0 | 11410.0 | 14522.5 | 16010.0 | 16915.0 | 17427.5 | 17552.5 | 17115.0 | 16440.0 | 15597.5 | 12175.0 | 15062.5 | 7804.0 | 113290.0 | 26954.0 | 18012.0 | 2256.0 | 8878.0 | 8077.0 | 12115.0 | 37606.0 | 1784.0 | 37011.0 | 12860.0 | 6180.0 | 3197.0 | 5582.0 | 234991.0 | 66613.0 | 301604.0 | 14319 | 6645 | 15200 | 19432 | 11376 | 30808 | 8555 | 23294 | 15958 | 15235 | 6002 | 1557 | 4255 | 11724 | 17244 | 18121 | 24686 | 25464 | 28022 | 5633 | 14219 | 14507 | 21356 | 301604 | Low | High |
| 12 | 2022 | 23740 | 2460 | 5800 | 265 | 940 | 1130 | 1870 | 620 | 1160 | 3500 | 6770 | 17440 | 2070 | 4100 | 345 | 770 | 890 | 1510 | 570 | 1110 | 2720 | 5670 | 603.099976 | 3310.199951 | 81.800003 | 3.6 | 78.800003 | 2707.100098 | 200.328506 | 429.891235 | 286.851013 | 459.618011 | 394.096741 | 342.017487 | 402.063995 | 457.423737 | 302.150757 | 192.752747 | 188.985748 | 391.059265 | 213.925003 | 333.005493 | 338.508240 | 107.830002 | 437.896240 | 342.504486 | 173.082993 | 368.586761 | 188.493744 | 186.207748 | 348.890259 | 362.155762 | 208.888000 | 201.230743 | 492.319244 | 695.994019 | 185.337250 | 241.535507 | 342.635986 | 281.004242 | 386.713257 | 217.796249 | 314.244263 | 5285.0 | 12522.5 | 15717.5 | 17320.0 | 18235.0 | 18725.0 | 18897.5 | 18440.0 | 17642.5 | 16700.0 | 13062.5 | 16185.0 | 8684.0 | 124270.0 | 29378.0 | 19527.0 | 2318.0 | 9773.0 | 8911.0 | 13110.0 | 40777.0 | 1798.0 | 40380.0 | 14016.0 | 7144.0 | 3283.0 | 6038.0 | 256748.0 | 72659.0 | 329407.0 | 16373 | 6805 | 16463 | 19567 | 12030 | 31597 | 9658 | 24320 | 18992 | 16985 | 6851 | 1743 | 4896 | 13124 | 18604 | 20252 | 26033 | 27240 | 31774 | 6671 | 15185 | 15489 | 23665 | 329407 | Low | High |
3.5.2 Create the Classified Column for GDP Auckland by 5 Grades.ΒΆ
labels = ['E', 'D', 'C', 'B', 'A']
bins = [merged_data1['GDP_Auckland'].min(), 70000, 85000, 95000, 110000, merged_data1['GDP_Auckland'].max()]
merged_data1['GDP_Auckland_Grade'] = pd.cut(merged_data1['GDP_Auckland'], bins=bins, labels=labels, include_lowest=True)
merged_data1[['GDP_Auckland', 'GDP_Auckland_Grade']]
| GDP_Auckland | GDP_Auckland_Grade | |
|---|---|---|
| 0 | 63382.0 | E |
| 1 | 65759.0 | E |
| 2 | 68978.0 | E |
| 3 | 71351.0 | D |
| 4 | 75444.0 | D |
| 5 | 81076.0 | D |
| 6 | 88139.0 | C |
| 7 | 93624.0 | C |
| 8 | 99794.0 | B |
| 9 | 105433.0 | B |
| 10 | 110312.0 | A |
| 11 | 113290.0 | A |
| 12 | 124270.0 | A |
merged_data1
| Year | job_creation_Auckland | job_creation_Bay_of_Plenty | job_creation_Canterbury | job_creation_Gisborne | job_creation_Hawkes_Bay | job_creation_Manawatu_Wanganui | job_creation_Otago | job_creation_Taranaki | job_creation_Tasman__Nelson__Marlborough__West_Coast | job_creation_Waikato | job_creation_Wellington | job_destruction_Auckland | job_destruction_Bay_of_Plenty | job_destruction_Canterbury | job_destruction_Gisborne | job_destruction_Hawkes_Bay | job_destruction_Manawatu_Wanganui | job_destruction_Otago | job_destruction_Taranaki | job_destruction_Tasman__Nelson__Marlborough__West_Coast | job_destruction_Waikato | job_destruction_Wellington | Not_in_Labour_Force | Working_Age_Population | Labour_Force_Participation_Rate | Unemployment_Rate | Employment_Rate | Total_Labour_Force | AVI_Auckland | AVI_Bay_of_Plenty | AVI_Canterbury | AVI_Gisborne_Hawkes_Bay | AVI_Marlborough_NelsonTasman_West_Coast | AVI_Manawatu_Wanganui_Taranaki | AVI_Northland | AVI_Otago_Southland | AVI_Waikato | AVI_Wellington | AVI_ids_Accounting | AVI_ids_Construction | AVI_ids_Education | AVI_ids_Health | AVI_ids_Hospitalty | AVI_ids_IT | AVI_ids_Manufacturing | AVI_ids_Primary | AVI_ids_Sales | AVI_ids_Other | AVI_ocp_Managers | AVI_ocp_Professionals | AVI_ocp_Tech_and_Trades | AVI_ocp_Community_and_Personal_Services | AVI_ocp_Clerical_and_Administration | AVI_ocp_Sales | AVI_ocp_Machinery_Drivers | AVI_ocp_Labourers | AVI_ocp_highly_skilled | AVI_ocp_skilled | AVI_ocp_semi_skilled | AVI_ocp_low_skilled | AVI_ocp_unskilled | AVI_ocp_Skilled | AVI_ocp_Unskilled | median_wage_15_19 | median_wage_20_24 | median_wage_25_29 | median_wage_30_34 | median_wage_35_39 | median_wage_40_44 | median_wage_45_49 | median_wage_50_54 | median_wage_55_59 | median_wage_60_64 | median_wage_65 | median_wage_Total_All_Ages | GDP_Northland | GDP_Auckland | GDP_Waikato | GDP_Bay_of_Plenty | GDP_Gisborne | GDP_Hawkes_Bay | GDP_Taranaki | GDP_Manawatu_Whanganui | GDP_Wellington | GDP_West_Coast | GDP_Canterbury | GDP_Otago | GDP_Southland | GDP_Marlborough | GDP_Tasman_Nelson | GDP_Total_North_Island | GDP_Total_South_Island | GDP_New_Zealand | GDP_Agriculture | GDP_Forestry__Fishing__and_Mining | GDP_Forestry__Fishing__Mining__Electricity__Gas__Water_and_Waste_Services | GDP_Primary_Manufacturing | GDP_Other_Manufacturing | GDP_Manufacturing | GDP_Electricity__Gas__Water__and_Waste_services | GDP_Construction | GDP_Wholesale_Trade | GDP_Retail_Trade | GDP_Accommodation_and_Food_Services | GDP_Accommodation | GDP_Food_and_beverage_services | GDP_Transport__Postal_and_Warehousing | GDP_Information_Media__Telecommunications_and_Other_Services | GDP_Financial_and_Insurance_Services | GDP_Rental__Hiring_and_Real_Estate_Services | GDP_Owner_Occupied_Property_Operation | GDP_Professional__Scientific__and_Technical_Services | GDP_Administrative_and_Support_Services | GDP_Public_Administration_and_Safety | GDP_Education_and_Training | GDP_Health_Care_and_Social_Assistance | GDP_Total_All_Industries | Unemployment Rate Classified | AVI Auckland Classified | GDP_Auckland_Grade | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2010 | 18130 | 1580 | 3320 | 250 | 830 | 840 | 1240 | 475 | 800 | 2730 | 4910 | 15380 | 1430 | 3470 | 195 | 850 | 980 | 1300 | 515 | 900 | 2270 | 5290 | 645.700012 | 2844.399902 | 77.300003 | 6.3 | 72.400002 | 2198.699951 | 100.000000 | 100.000000 | 100.000000 | 100.000000 | 100.000000 | 100.000000 | 100.000000 | 100.000000 | 100.000000 | 100.000000 | 100.000000 | 100.000000 | 100.000000 | 100.000000 | 100.000000 | 100.000000 | 100.000000 | 100.000000 | 100.000000 | 100.000000 | 100.000000 | 100.000000 | 100.000000 | 100.000000 | 100.000000 | 100.000000 | 100.000000 | 100.000000 | 100.000000 | 100.000000 | 100.000000 | 100.000000 | 100.000000 | 100.000000 | 100.000000 | 3060.0 | 8050.0 | 10582.5 | 11865.0 | 12315.0 | 12137.5 | 12065.0 | 11905.0 | 11437.5 | 10515.0 | 7245.0 | 10632.5 | 4543.0 | 63382.0 | 15258.0 | 9409.0 | 1324.0 | 5150.0 | 7665.0 | 7544.0 | 25113.0 | 1351.0 | 22229.0 | 7894.0 | 4168.0 | 1794.0 | 3296.0 | 139388.0 | 40731.0 | 180119.0 | 8654 | 6651 | 12720 | 13001 | 7925 | 20926 | 6069 | 10464 | 9869 | 8292 | 3789 | 1226 | 2563 | 7929 | 12490 | 11119 | 13413 | 12192 | 14735 | 3633 | 8732 | 9204 | 11958 | 180119 | High | Low | E |
| 1 | 2011 | 17310 | 1600 | 4100 | 190 | 710 | 810 | 1140 | 480 | 850 | 2220 | 5310 | 14540 | 1370 | 3730 | 190 | 690 | 890 | 1110 | 490 | 760 | 1920 | 5120 | 655.799988 | 2866.699951 | 77.099998 | 6.4 | 72.199997 | 2211.000000 | 101.537247 | 103.784752 | 131.811493 | 107.877747 | 111.249252 | 104.018753 | 109.501999 | 116.044502 | 105.596497 | 101.322998 | 106.247749 | 115.751999 | 92.198753 | 104.268753 | 107.144249 | 102.214249 | 111.413254 | 123.854248 | 101.984749 | 108.947250 | 103.917252 | 106.695747 | 121.152000 | 115.340752 | 107.325996 | 105.281250 | 113.408501 | 118.313751 | 105.897751 | 103.693748 | 126.957001 | 111.033752 | 104.367249 | 108.664749 | 109.756752 | 3152.5 | 8162.5 | 10835.0 | 12155.0 | 12712.5 | 12565.0 | 12462.5 | 12290.0 | 11845.0 | 10967.5 | 7752.5 | 10972.5 | 4842.0 | 65759.0 | 16075.0 | 9947.0 | 1394.0 | 5447.0 | 7977.0 | 7888.0 | 25501.0 | 1427.0 | 23219.0 | 8171.0 | 4498.0 | 1813.0 | 3446.0 | 144830.0 | 42574.0 | 187404.0 | 10643 | 6938 | 13291 | 13860 | 8191 | 22051 | 6353 | 10470 | 10081 | 8469 | 3857 | 1248 | 2609 | 8758 | 12421 | 9975 | 13648 | 13484 | 15529 | 3959 | 8835 | 9595 | 12337 | 187404 | High | Low | E |
| 2 | 2012 | 17260 | 1560 | 4580 | 195 | 820 | 1040 | 1230 | 510 | 800 | 2710 | 5880 | 14410 | 1510 | 3710 | 160 | 920 | 940 | 1200 | 450 | 790 | 2290 | 5080 | 652.500000 | 2876.300049 | 77.300003 | 6.3 | 72.400002 | 2223.800049 | 104.071747 | 110.633003 | 170.327499 | 120.652748 | 129.169006 | 117.805748 | 115.172501 | 138.578751 | 110.881500 | 102.747253 | 109.499001 | 147.468002 | 83.279999 | 119.278000 | 112.457253 | 97.901749 | 125.172997 | 148.839752 | 105.112251 | 113.703247 | 106.715752 | 107.819504 | 142.382004 | 133.338745 | 113.677498 | 107.552750 | 137.171494 | 145.013245 | 107.701500 | 110.109497 | 150.747253 | 120.932503 | 116.904999 | 113.887749 | 120.466751 | 3257.5 | 8370.0 | 11112.5 | 12452.5 | 13102.5 | 13020.0 | 12925.0 | 12735.0 | 12280.0 | 11435.0 | 8245.0 | 11335.0 | 4922.0 | 68978.0 | 17102.0 | 10321.0 | 1432.0 | 5575.0 | 7896.0 | 7997.0 | 26724.0 | 1517.0 | 24316.0 | 8402.0 | 4599.0 | 1919.0 | 3503.0 | 150947.0 | 44256.0 | 195203.0 | 10622 | 6772 | 13371 | 15298 | 8017 | 23315 | 6600 | 10804 | 10526 | 8878 | 3940 | 1245 | 2696 | 9331 | 12960 | 10625 | 14757 | 14344 | 15869 | 4172 | 9041 | 9985 | 12665 | 195203 | High | Low | E |
| 3 | 2013 | 18320 | 1800 | 4360 | 305 | 790 | 770 | 1290 | 660 | 830 | 2110 | 5820 | 13340 | 1470 | 3770 | 245 | 710 | 780 | 1070 | 480 | 760 | 1950 | 4720 | 666.900024 | 2878.800049 | 76.800003 | 6.6 | 71.800003 | 2211.899902 | 116.276497 | 130.563995 | 191.616257 | 127.156250 | 136.180756 | 137.263504 | 120.304001 | 150.940750 | 123.468246 | 106.194748 | 112.277748 | 184.800247 | 92.263496 | 123.594498 | 134.522247 | 92.378502 | 153.481247 | 158.919006 | 113.936996 | 122.010750 | 116.591751 | 108.565002 | 171.246750 | 157.548996 | 120.545998 | 118.168999 | 180.684998 | 190.806747 | 110.852997 | 123.290001 | 181.366257 | 137.462250 | 138.647995 | 123.331497 | 138.688507 | 3290.0 | 8555.0 | 11340.0 | 12705.0 | 13407.5 | 13420.0 | 13267.5 | 13065.0 | 12647.5 | 11850.0 | 8620.0 | 11617.5 | 4899.0 | 71351.0 | 16725.0 | 10490.0 | 1427.0 | 5730.0 | 7967.0 | 7999.0 | 27149.0 | 1449.0 | 25371.0 | 8526.0 | 4432.0 | 2001.0 | 3581.0 | 153739.0 | 45360.0 | 199099.0 | 9130 | 6610 | 13764 | 15133 | 7938 | 23071 | 7154 | 11541 | 10530 | 9489 | 4124 | 1270 | 2855 | 9790 | 13188 | 10921 | 15134 | 15260 | 16555 | 4165 | 9147 | 10154 | 13133 | 199099 | High | Low | D |
| 4 | 2014 | 18870 | 1580 | 4710 | 285 | 690 | 860 | 1380 | 550 | 1010 | 2350 | 6050 | 15940 | 1620 | 3600 | 245 | 720 | 800 | 1180 | 600 | 690 | 2400 | 4680 | 641.799988 | 2899.399902 | 77.900002 | 6.0 | 73.199997 | 2257.600098 | 133.691757 | 154.134003 | 220.149506 | 143.167496 | 165.084747 | 144.570755 | 141.191498 | 188.934753 | 140.002243 | 111.706253 | 121.781998 | 230.177505 | 106.677002 | 129.583496 | 162.815247 | 95.911003 | 181.682999 | 183.427505 | 128.405243 | 121.794746 | 135.623993 | 115.741753 | 207.521744 | 180.007996 | 132.238754 | 131.477249 | 223.298996 | 249.287994 | 120.819504 | 142.899002 | 209.124252 | 158.863007 | 167.512497 | 138.882507 | 162.422501 | 3392.5 | 8820.0 | 11590.0 | 13025.0 | 13767.5 | 13877.5 | 13655.0 | 13475.0 | 13017.5 | 12245.0 | 9032.5 | 11925.0 | 5363.0 | 75444.0 | 18690.0 | 11030.0 | 1474.0 | 6044.0 | 8508.0 | 8558.0 | 28348.0 | 1548.0 | 28198.0 | 9224.0 | 5075.0 | 2224.0 | 3873.0 | 163457.0 | 50143.0 | 213600.0 | 13111 | 6842 | 14266 | 15619 | 8209 | 23828 | 7424 | 12398 | 11592 | 9987 | 4368 | 1336 | 3034 | 10356 | 13731 | 12265 | 16128 | 16147 | 17763 | 4283 | 9399 | 10503 | 13473 | 213600 | High | Low | D |
| 5 | 2015 | 18550 | 1920 | 4540 | 180 | 780 | 720 | 1340 | 485 | 770 | 2440 | 5750 | 16200 | 1390 | 3960 | 205 | 800 | 780 | 1240 | 550 | 970 | 2510 | 4920 | 625.299988 | 2953.699951 | 78.800003 | 5.6 | 74.400002 | 2328.300049 | 145.303253 | 172.999496 | 209.960754 | 155.774750 | 165.059494 | 143.589996 | 150.659256 | 198.530746 | 137.801743 | 107.152000 | 132.479752 | 241.839005 | 129.024750 | 136.664001 | 171.364746 | 84.667503 | 188.766251 | 170.084000 | 129.656998 | 125.584000 | 142.624496 | 117.330002 | 215.301498 | 185.268997 | 139.214996 | 135.110001 | 226.864502 | 258.147491 | 122.263000 | 153.565750 | 220.054993 | 163.960999 | 173.757248 | 142.871506 | 167.606003 | 3470.0 | 9025.0 | 11745.0 | 13300.0 | 14057.5 | 14255.0 | 14037.5 | 13852.5 | 13335.0 | 12595.0 | 9360.0 | 12192.5 | 5555.0 | 81076.0 | 18754.0 | 11291.0 | 1541.0 | 6176.0 | 8329.0 | 8681.0 | 29588.0 | 1479.0 | 29434.0 | 9427.0 | 4841.0 | 2338.0 | 3996.0 | 170990.0 | 51516.0 | 222506.0 | 8240 | 6936 | 14531 | 18113 | 8753 | 26866 | 7595 | 13833 | 12486 | 10491 | 4763 | 1364 | 3420 | 11424 | 14452 | 13514 | 16526 | 17148 | 18570 | 4556 | 9824 | 10998 | 14263 | 222506 | Low | Low | D |
| 6 | 2016 | 18440 | 2120 | 5510 | 285 | 730 | 1000 | 1560 | 585 | 830 | 2820 | 5990 | 14920 | 2220 | 4270 | 285 | 700 | 800 | 1270 | 600 | 880 | 2130 | 5350 | 646.799988 | 3022.500000 | 78.599998 | 5.6 | 74.199997 | 2375.800049 | 165.826004 | 216.454498 | 201.011993 | 185.867996 | 197.029999 | 155.698746 | 170.623505 | 227.022003 | 159.732498 | 118.360252 | 147.285751 | 278.177765 | 139.579498 | 144.005997 | 196.753754 | 84.843498 | 219.529999 | 196.574997 | 141.570251 | 154.535995 | 158.963257 | 124.945999 | 250.199753 | 201.402496 | 156.169006 | 144.600494 | 269.366760 | 312.987000 | 131.723007 | 172.766006 | 252.503250 | 181.384995 | 202.331497 | 157.087494 | 188.636505 | 3570.0 | 9267.5 | 11977.5 | 13605.0 | 14405.0 | 14702.5 | 14470.0 | 14267.5 | 13745.0 | 12967.5 | 9682.5 | 12505.0 | 5994.0 | 88139.0 | 19413.0 | 12287.0 | 1638.0 | 6444.0 | 7221.0 | 8944.0 | 30654.0 | 1380.0 | 30338.0 | 10057.0 | 4693.0 | 2440.0 | 4216.0 | 180735.0 | 53124.0 | 233859.0 | 7691 | 6107 | 13779 | 19703 | 9105 | 28808 | 7673 | 15170 | 12856 | 11083 | 5327 | 1560 | 3745 | 12430 | 15012 | 13962 | 17919 | 18106 | 19932 | 4889 | 10322 | 11431 | 15164 | 233859 | Low | High | C |
| 7 | 2017 | 22050 | 2090 | 5140 | 265 | 950 | 1120 | 1640 | 655 | 910 | 2640 | 8000 | 15250 | 1710 | 4870 | 260 | 710 | 860 | 1200 | 590 | 630 | 2260 | 6650 | 608.700012 | 3103.300049 | 80.400002 | 5.3 | 76.099998 | 2494.699951 | 175.229996 | 243.526505 | 211.830994 | 214.937744 | 250.458755 | 179.497757 | 211.601257 | 282.455750 | 195.681747 | 127.823753 | 148.992752 | 310.851257 | 149.963501 | 158.511505 | 214.954742 | 77.326752 | 279.994263 | 241.283005 | 147.703995 | 228.611252 | 170.497757 | 130.923248 | 269.986237 | 214.666000 | 163.593002 | 159.660248 | 348.409241 | 390.851257 | 139.533249 | 182.877243 | 272.595490 | 207.358246 | 232.789993 | 166.677505 | 216.222748 | 3822.5 | 9607.5 | 12285.0 | 13912.5 | 14750.0 | 15095.0 | 14952.5 | 14665.0 | 14147.5 | 13327.5 | 9995.0 | 12832.5 | 6428.0 | 93624.0 | 20709.0 | 13820.0 | 1707.0 | 7011.0 | 7279.0 | 9358.0 | 32240.0 | 1450.0 | 31189.0 | 10745.0 | 5122.0 | 2604.0 | 4492.0 | 192176.0 | 55602.0 | 247778.0 | 11108 | 6438 | 14320 | 17562 | 9820 | 27381 | 7882 | 16763 | 12843 | 12294 | 5947 | 1830 | 4213 | 12263 | 15447 | 14487 | 19690 | 19297 | 21780 | 5330 | 10902 | 11924 | 15905 | 247778 | Low | High | C |
| 8 | 2018 | 22660 | 2360 | 6720 | 290 | 860 | 970 | 1720 | 590 | 880 | 2940 | 7220 | 17380 | 2060 | 5530 | 217 | 730 | 1150 | 1560 | 485 | 770 | 2680 | 6270 | 600.799988 | 3165.199951 | 81.000000 | 4.8 | 77.099998 | 2564.300049 | 178.728500 | 283.244751 | 211.246750 | 258.770752 | 284.169250 | 210.156754 | 243.121506 | 327.308746 | 224.371002 | 139.305252 | 156.547256 | 319.922485 | 155.553253 | 189.209747 | 231.892502 | 86.007004 | 302.975006 | 271.968262 | 152.390747 | 249.154999 | 177.337997 | 140.409256 | 275.519501 | 234.194504 | 171.591003 | 166.481247 | 376.268005 | 453.763000 | 150.222244 | 187.268250 | 277.112244 | 222.082993 | 257.696014 | 175.389252 | 234.239502 | 4052.5 | 10072.5 | 12815.0 | 14397.5 | 15257.5 | 15665.0 | 15582.5 | 15247.5 | 14727.5 | 13915.0 | 10507.5 | 13357.5 | 6970.0 | 99794.0 | 22583.0 | 15106.0 | 1844.0 | 7567.0 | 8129.0 | 10123.0 | 33610.0 | 1632.0 | 33555.0 | 11754.0 | 5609.0 | 2798.0 | 4835.0 | 205727.0 | 60184.0 | 265911.0 | 12610 | 7422 | 15426 | 19546 | 10092 | 29638 | 8004 | 19009 | 13977 | 12896 | 6247 | 1922 | 4440 | 12909 | 15889 | 16262 | 20538 | 20772 | 23382 | 5728 | 11531 | 12275 | 16708 | 265911 | Low | High | B |
| 9 | 2019 | 19170 | 2290 | 5700 | 270 | 900 | 980 | 1820 | 725 | 1010 | 3260 | 6840 | 20200 | 2130 | 4860 | 225 | 920 | 980 | 1630 | 690 | 920 | 2600 | 6690 | 608.700012 | 3219.500000 | 81.099998 | 4.5 | 77.500000 | 2610.899902 | 170.895752 | 290.044250 | 197.054245 | 286.093994 | 286.961487 | 212.753998 | 235.375000 | 340.894012 | 224.387756 | 148.065750 | 152.729004 | 310.461243 | 152.830002 | 194.334244 | 226.292252 | 91.447746 | 286.351746 | 268.006012 | 147.673248 | 282.870514 | 173.587006 | 141.817245 | 267.490753 | 244.368256 | 169.208496 | 154.995743 | 349.331238 | 473.024994 | 150.126999 | 187.576508 | 273.232513 | 209.394745 | 263.279999 | 176.359497 | 226.138748 | 4252.5 | 10460.0 | 13295.0 | 14860.0 | 15780.0 | 16160.0 | 16220.0 | 15822.5 | 15237.5 | 14417.5 | 11020.0 | 13852.5 | 7238.0 | 105433.0 | 24349.0 | 16322.0 | 1999.0 | 7886.0 | 8245.0 | 10744.0 | 34849.0 | 1685.0 | 35066.0 | 12484.0 | 5932.0 | 2966.0 | 5162.0 | 217065.0 | 63295.0 | 280360.0 | 12655 | 7652 | 16141 | 19888 | 10567 | 30455 | 8489 | 20314 | 14394 | 13503 | 6471 | 2105 | 4609 | 13609 | 16734 | 17161 | 21988 | 22127 | 25041 | 6116 | 12279 | 13099 | 18030 | 280360 | Low | High | B |
| 10 | 2020 | 20070 | 1950 | 5130 | 250 | 940 | 990 | 1470 | 680 | 1090 | 2800 | 6460 | 17190 | 1740 | 4740 | 175 | 750 | 1010 | 1410 | 570 | 1000 | 2400 | 5170 | 622.200012 | 3272.399902 | 81.000000 | 4.3 | 77.500000 | 2650.300049 | 146.184753 | 271.098236 | 167.698502 | 316.912262 | 272.200256 | 218.504745 | 242.593246 | 294.454254 | 207.248245 | 139.345505 | 134.226257 | 288.206512 | 141.802505 | 193.320496 | 193.007751 | 77.951500 | 267.991241 | 261.999756 | 123.960999 | 272.392761 | 156.774246 | 128.820496 | 247.310501 | 216.382996 | 132.521744 | 130.047745 | 342.761261 | 454.219513 | 136.427994 | 165.027496 | 256.376495 | 181.031250 | 236.547256 | 154.667007 | 199.125748 | 4595.0 | 10627.5 | 13565.0 | 15062.5 | 15942.5 | 16417.5 | 16535.0 | 16147.5 | 15552.5 | 14747.5 | 11495.0 | 14167.5 | 7712.0 | 110312.0 | 25993.0 | 17283.0 | 2056.0 | 8397.0 | 8483.0 | 11344.0 | 37561.0 | 1725.0 | 37099.0 | 13097.0 | 6093.0 | 3135.0 | 5487.0 | 229139.0 | 66638.0 | 295777.0 | 13866 | 7383 | 16375 | 20524 | 10733 | 31257 | 8992 | 22400 | 14558 | 13983 | 6823 | 2140 | 4875 | 13817 | 17359 | 17594 | 23654 | 23810 | 27100 | 6289 | 13457 | 13795 | 19447 | 295777 | Low | Low | A |
| 11 | 2021 | 22980 | 3300 | 6180 | 270 | 1390 | 1090 | 1780 | 790 | 1160 | 3800 | 7750 | 16450 | 2340 | 4950 | 185 | 770 | 930 | 1390 | 660 | 860 | 2800 | 5470 | 643.700012 | 3318.600098 | 80.599998 | 4.9 | 76.599998 | 2674.899902 | 189.515747 | 392.755005 | 248.804749 | 451.379486 | 381.414490 | 306.777740 | 344.608765 | 396.520996 | 291.072754 | 195.253494 | 177.007507 | 389.319000 | 197.629745 | 294.096985 | 233.626251 | 115.649002 | 401.498993 | 338.093994 | 157.812500 | 369.927002 | 191.849243 | 180.051498 | 329.804504 | 282.026245 | 194.417496 | 180.381500 | 487.175751 | 639.705994 | 183.621002 | 209.551743 | 336.203491 | 257.895996 | 337.497498 | 211.006256 | 282.832764 | 4875.0 | 11410.0 | 14522.5 | 16010.0 | 16915.0 | 17427.5 | 17552.5 | 17115.0 | 16440.0 | 15597.5 | 12175.0 | 15062.5 | 7804.0 | 113290.0 | 26954.0 | 18012.0 | 2256.0 | 8878.0 | 8077.0 | 12115.0 | 37606.0 | 1784.0 | 37011.0 | 12860.0 | 6180.0 | 3197.0 | 5582.0 | 234991.0 | 66613.0 | 301604.0 | 14319 | 6645 | 15200 | 19432 | 11376 | 30808 | 8555 | 23294 | 15958 | 15235 | 6002 | 1557 | 4255 | 11724 | 17244 | 18121 | 24686 | 25464 | 28022 | 5633 | 14219 | 14507 | 21356 | 301604 | Low | High | A |
| 12 | 2022 | 23740 | 2460 | 5800 | 265 | 940 | 1130 | 1870 | 620 | 1160 | 3500 | 6770 | 17440 | 2070 | 4100 | 345 | 770 | 890 | 1510 | 570 | 1110 | 2720 | 5670 | 603.099976 | 3310.199951 | 81.800003 | 3.6 | 78.800003 | 2707.100098 | 200.328506 | 429.891235 | 286.851013 | 459.618011 | 394.096741 | 342.017487 | 402.063995 | 457.423737 | 302.150757 | 192.752747 | 188.985748 | 391.059265 | 213.925003 | 333.005493 | 338.508240 | 107.830002 | 437.896240 | 342.504486 | 173.082993 | 368.586761 | 188.493744 | 186.207748 | 348.890259 | 362.155762 | 208.888000 | 201.230743 | 492.319244 | 695.994019 | 185.337250 | 241.535507 | 342.635986 | 281.004242 | 386.713257 | 217.796249 | 314.244263 | 5285.0 | 12522.5 | 15717.5 | 17320.0 | 18235.0 | 18725.0 | 18897.5 | 18440.0 | 17642.5 | 16700.0 | 13062.5 | 16185.0 | 8684.0 | 124270.0 | 29378.0 | 19527.0 | 2318.0 | 9773.0 | 8911.0 | 13110.0 | 40777.0 | 1798.0 | 40380.0 | 14016.0 | 7144.0 | 3283.0 | 6038.0 | 256748.0 | 72659.0 | 329407.0 | 16373 | 6805 | 16463 | 19567 | 12030 | 31597 | 9658 | 24320 | 18992 | 16985 | 6851 | 1743 | 4896 | 13124 | 18604 | 20252 | 26033 | 27240 | 31774 | 6671 | 15185 | 15489 | 23665 | 329407 | Low | High | A |
3.5.3 Display the Correlation of Overall Unemployment Rate with Auckland's AVI and GDP by Pivot Table.ΒΆ
merged_data1.pivot_table(values='AVI_Auckland',index=['GDP_Auckland_Grade', 'AVI Auckland Classified'], aggfunc='mean' )
| AVI_Auckland | ||
|---|---|---|
| GDP_Auckland_Grade | AVI Auckland Classified | |
| E | Low | 101.869667 |
| D | Low | 131.757156 |
| C | High | 170.528000 |
| B | High | 174.812134 |
| A | High | 194.922119 |
| Low | 146.184753 |
When categorizing Auckland's GDP levels from the highest (A) to the lowest (E) and dividing AVI Auckland by the median, we observe an interesting pattern: a high GDP does not necessarily correspond to a low AVI (indicating fewer job vacancies). This suggests that high GDP industries might not be directly related to labor-intensive support, meaning they could generate substantial economic output without a proportional increase in job vacancies.
merged_data1.pivot_table(values=['AVI_Auckland'], index=['Unemployment Rate Classified','GDP_Auckland_Grade'], columns='AVI Auckland Classified')
| AVI_Auckland | |||
|---|---|---|---|
| AVI Auckland Classified | High | Low | |
| Unemployment Rate Classified | GDP_Auckland_Grade | ||
| High | E | NaN | 101.869667 |
| D | NaN | 124.984131 | |
| Low | D | NaN | 145.303253 |
| C | 170.528000 | NaN | |
| B | 174.812134 | NaN | |
| A | 194.922119 | 146.184753 | |
Including the overall unemployment rate of New Zealand in the analysis, we observe that higher unemployment rates correlate with lower GDP for Auckland. This matchs with our general understanding that higher unemployment negatively impacts economic output.
merged_data1.pivot_table(values='AVI_Auckland', index=['Unemployment Rate Classified','GDP_Auckland_Grade'], columns='AVI Auckland Classified', aggfunc=len, margins=True)
| AVI Auckland Classified | High | Low | All | |
|---|---|---|---|---|
| Unemployment Rate Classified | GDP_Auckland_Grade | |||
| High | E | NaN | 3.0 | 3 |
| D | NaN | 2.0 | 2 | |
| Low | D | NaN | 1.0 | 1 |
| C | 2.0 | NaN | 2 | |
| B | 2.0 | NaN | 2 | |
| A | 2.0 | 1.0 | 3 | |
| All | 6.0 | 7.0 | 13 |
Counting the number of entries in each category, we find a total of 13 observations. Among these, 6 have high AVI and 7 have low AVI. Additionally, the distribution of GDP Auckland across each grade is clearly visible.
crosstab_AVI_Auckland = pd.crosstab(index=[merged_data1['Unemployment Rate Classified'], merged_data1['GDP_Auckland_Grade']],
columns=merged_data1['AVI Auckland Classified'],
values=merged_data1['AVI_Auckland'],
aggfunc='count',
margins=True)
crosstab_AVI_Auckland
| AVI Auckland Classified | High | Low | All | |
|---|---|---|---|---|
| Unemployment Rate Classified | GDP_Auckland_Grade | |||
| High | E | 0 | 3 | 3.0 |
| D | 0 | 2 | 2.0 | |
| C | 0 | 0 | NaN | |
| B | 0 | 0 | NaN | |
| A | 0 | 0 | NaN | |
| Low | E | 0 | 0 | NaN |
| D | 0 | 1 | 1.0 | |
| C | 2 | 0 | 2.0 | |
| B | 2 | 0 | 2.0 | |
| A | 2 | 1 | 3.0 | |
| All | 6 | 7 | 13.0 |
By presenting the same content using a cross table, we can see that the crosstabulation expands all data, displaying the frequency distribution of variables in a matrix format.
3.5.4 Visualize the Correlation of Overall Unemployment Rate with Auckland's AVI and GDP.ΒΆ
Unemployment_AVIAuckland = merged_data1.groupby('Unemployment Rate Classified')['GDP_Auckland'].sum()
Unemployment_AVIAuckland.plot(kind='bar', figsize=(10, 6), color='Orange', edgecolor='black')
plt.title('Unemployment Rate in New Zealand Relates to GDP Auckland')
plt.xlabel('Unemployment Rate Classified')
plt.ylabel('GDP Auckland')
Text(0, 0.5, 'GDP Auckland')
Based on the chart, we can see that when summing the GDP of Auckland for both high and low unemployment rates, the GDP is significantly lower for the group with high unemployment rates. Conversely, the GDP is higher for the group with low unemployment rates. This implies that high unemployment rates in New Zealand lead to lower GDP in Auckland, and vice versa.
Unemployment_AVIAuckland = merged_data1.groupby('Unemployment Rate Classified')['AVI_Auckland'].sum()
Unemployment_AVIAuckland.plot(kind='bar', figsize=(10, 6), color='blue', edgecolor='black')
plt.title('Unemployment Rate in New Zealand Relates to AVI Auckland')
plt.xlabel('Unemployment Rate Classified')
plt.ylabel('AVI Auckland')
Text(0, 0.5, 'AVI Auckland')
This table similarly categorizes data by New Zealand's unemployment rates. High unemployment rates lead to low AVI, while low unemployment rates lead to high AVI. This implies that lower unemployment rates correlate with more job opportunities, or conversely, that more job opportunities (high AVI) can result in lower unemployment rates.
3.6 Does the Job Creation in Auckland from 1999 to 2022 Remain Positive, Making Auckland the City with the Most Economic Contribution?ΒΆ
3.6.1 Build a pivot table that classifies job creation and job destruction in Auckland from 1999 to 2022,ΒΆ
regionjob
| Date | Type | Auckland | Waikato | Bay of Plenty | Gisborne | Hawke's Bay | Taranaki | Manawatu-Wanganui | Wellington | Tasman, Nelson, Marlborough, West Coast | Canterbury | Otago | Year | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 1999Q3 | Job Creation | 2710 | 340 | 370 | 35 | 120 | 90 | 170 | 1080 | 130 | 670 | 170 | 1999 |
| 2 | 1999Q4 | Job Creation | 3040 | 500 | 260 | 30 | 150 | 120 | 220 | 1100 | 100 | 820 | 220 | 1999 |
| 3 | 2000Q1 | Job Creation | 3340 | 610 | 420 | 45 | 210 | 210 | 240 | 1530 | 130 | 750 | 210 | 2000 |
| 4 | 2000Q2 | Job Creation | 3700 | 760 | 400 | 25 | 210 | 180 | 260 | 1820 | 150 | 1140 | 380 | 2000 |
| 5 | 2000Q3 | Job Creation | 3340 | 430 | 320 | 40 | 180 | 120 | 250 | 1530 | 130 | 690 | 200 | 2000 |
| 6 | 2000Q4 | Job Creation | 3060 | 410 | 280 | 40 | 280 | 120 | 220 | 1170 | 130 | 710 | 190 | 2000 |
| 7 | 2001Q1 | Job Creation | 4760 | 480 | 420 | 75 | 230 | 160 | 230 | 2870 | 150 | 2180 | 270 | 2001 |
| 8 | 2001Q2 | Job Creation | 4000 | 640 | 350 | 30 | 150 | 140 | 230 | 1620 | 190 | 890 | 300 | 2001 |
| 9 | 2001Q3 | Job Creation | 3280 | 530 | 290 | 30 | 160 | 140 | 220 | 1200 | 120 | 790 | 230 | 2001 |
| 10 | 2001Q4 | Job Creation | 3060 | 540 | 290 | 30 | 270 | 160 | 170 | 1130 | 130 | 720 | 200 | 2001 |
| 11 | 2002Q1 | Job Creation | 4420 | 610 | 280 | 65 | 400 | 190 | 260 | 2280 | 180 | 750 | 200 | 2002 |
| 12 | 2002Q2 | Job Creation | 3130 | 510 | 780 | 45 | 390 | 160 | 180 | 1130 | 150 | 730 | 200 | 2002 |
| 13 | 2002Q3 | Job Creation | 3680 | 520 | 310 | 45 | 140 | 150 | 200 | 1390 | 150 | 730 | 260 | 2002 |
| 14 | 2002Q4 | Job Creation | 3490 | 440 | 310 | 35 | 240 | 120 | 210 | 1010 | 140 | 730 | 180 | 2002 |
| 15 | 2003Q1 | Job Creation | 2610 | 320 | 290 | 35 | 250 | 210 | 240 | 840 | 160 | 670 | 300 | 2003 |
| 16 | 2003Q2 | Job Creation | 5190 | 490 | 1020 | 45 | 160 | 150 | 270 | 1790 | 200 | 900 | 280 | 2003 |
| 17 | 2003Q3 | Job Creation | 3920 | 700 | 300 | 30 | 200 | 210 | 310 | 1300 | 190 | 860 | 290 | 2003 |
| 18 | 2003Q4 | Job Creation | 2870 | 710 | 330 | 40 | 210 | 140 | 220 | 900 | 160 | 840 | 270 | 2003 |
| 19 | 2004Q1 | Job Creation | 3010 | 460 | 410 | 40 | 290 | 120 | 230 | 1140 | 180 | 790 | 340 | 2004 |
| 20 | 2004Q2 | Job Creation | 4860 | 990 | 560 | 55 | 200 | 270 | 280 | 1560 | 190 | 1010 | 280 | 2004 |
| 21 | 2004Q3 | Job Creation | 3290 | 580 | 330 | 20 | 210 | 150 | 190 | 1230 | 140 | 790 | 260 | 2004 |
| 22 | 2004Q4 | Job Creation | 3370 | 690 | 330 | 50 | 130 | 160 | 260 | 1060 | 190 | 820 | 280 | 2004 |
| 23 | 2005Q1 | Job Creation | 3700 | 580 | 360 | 85 | 240 | 190 | 380 | 1590 | 180 | 940 | 260 | 2005 |
| 24 | 2005Q2 | Job Creation | 4200 | 620 | 580 | 45 | 280 | 180 | 270 | 1440 | 170 | 1170 | 310 | 2005 |
| 25 | 2005Q3 | Job Creation | 3880 | 500 | 390 | 40 | 140 | 110 | 280 | 1140 | 190 | 900 | 260 | 2005 |
| 26 | 2005Q4 | Job Creation | 3810 | 490 | 390 | 30 | 210 | 170 | 250 | 1350 | 260 | 890 | 210 | 2005 |
| 27 | 2006Q1 | Job Creation | 3920 | 480 | 330 | 50 | 210 | 170 | 230 | 1630 | 220 | 1000 | 390 | 2006 |
| 28 | 2006Q2 | Job Creation | 5060 | 840 | 640 | 50 | 430 | 190 | 240 | 1620 | 290 | 1090 | 390 | 2006 |
| 29 | 2006Q3 | Job Creation | 4030 | 650 | 400 | 70 | 170 | 120 | 240 | 1360 | 190 | 970 | 410 | 2006 |
| 30 | 2006Q4 | Job Creation | 3420 | 570 | 370 | 45 | 180 | 100 | 190 | 1380 | 200 | 870 | 270 | 2006 |
| 31 | 2007Q1 | Job Creation | 3700 | 490 | 380 | 45 | 190 | 120 | 270 | 1210 | 190 | 930 | 280 | 2007 |
| 32 | 2007Q2 | Job Creation | 4960 | 630 | 670 | 90 | 230 | 160 | 240 | 1850 | 250 | 1380 | 400 | 2007 |
| 33 | 2007Q3 | Job Creation | 3980 | 820 | 420 | 85 | 200 | 170 | 240 | 1390 | 220 | 950 | 330 | 2007 |
| 34 | 2007Q4 | Job Creation | 4740 | 880 | 400 | 75 | 220 | 170 | 210 | 1510 | 310 | 940 | 310 | 2007 |
| 35 | 2008Q1 | Job Creation | 3890 | 600 | 400 | 60 | 310 | 160 | 220 | 1400 | 210 | 1080 | 430 | 2008 |
| 36 | 2008Q2 | Job Creation | 5430 | 750 | 690 | 45 | 300 | 230 | 610 | 1920 | 350 | 1340 | 490 | 2008 |
| 37 | 2008Q3 | Job Creation | 3260 | 480 | 480 | 50 | 150 | 220 | 210 | 1270 | 200 | 840 | 260 | 2008 |
| 38 | 2008Q4 | Job Creation | 3360 | 680 | 380 | 50 | 220 | 140 | 200 | 1130 | 250 | 940 | 300 | 2008 |
| 39 | 2009Q1 | Job Creation | 3360 | 500 | 320 | 65 | 220 | 150 | 310 | 1320 | 360 | 840 | 340 | 2009 |
| 40 | 2009Q2 | Job Creation | 3820 | 690 | 790 | 30 | 150 | 190 | 240 | 2420 | 240 | 1250 | 310 | 2009 |
| 41 | 2009Q3 | Job Creation | 3150 | 440 | 240 | 30 | 140 | 110 | 160 | 890 | 160 | 750 | 230 | 2009 |
| 42 | 2009Q4 | Job Creation | 3060 | 470 | 310 | 45 | 210 | 100 | 180 | 980 | 210 | 760 | 230 | 2009 |
| 43 | 2010Q1 | Job Creation | 4730 | 610 | 330 | 60 | 180 | 150 | 220 | 1100 | 170 | 660 | 290 | 2010 |
| 44 | 2010Q2 | Job Creation | 4920 | 830 | 620 | 50 | 270 | 140 | 230 | 1650 | 250 | 1010 | 370 | 2010 |
| 45 | 2010Q3 | Job Creation | 5030 | 610 | 330 | 30 | 160 | 85 | 230 | 1100 | 190 | 890 | 290 | 2010 |
| 46 | 2010Q4 | Job Creation | 3450 | 680 | 300 | 110 | 220 | 100 | 160 | 1060 | 190 | 760 | 290 | 2010 |
| 47 | 2011Q1 | Job Creation | 5480 | 440 | 460 | 55 | 190 | 110 | 230 | 1440 | 220 | 1110 | 290 | 2011 |
| 48 | 2011Q2 | Job Creation | 4910 | 640 | 560 | 55 | 190 | 110 | 230 | 1590 | 230 | 1030 | 340 | 2011 |
| 49 | 2011Q3 | Job Creation | 3590 | 470 | 300 | 35 | 150 | 110 | 150 | 1080 | 180 | 880 | 240 | 2011 |
| 50 | 2011Q4 | Job Creation | 3330 | 670 | 280 | 45 | 180 | 150 | 200 | 1200 | 220 | 1080 | 270 | 2011 |
| 51 | 2012Q1 | Job Creation | 5090 | 790 | 250 | 35 | 230 | 130 | 290 | 1600 | 230 | 1280 | 300 | 2012 |
| 52 | 2012Q2 | Job Creation | 4700 | 870 | 590 | 55 | 240 | 110 | 320 | 1700 | 200 | 1140 | 310 | 2012 |
| 53 | 2012Q3 | Job Creation | 3090 | 440 | 280 | 45 | 140 | 150 | 210 | 1110 | 160 | 950 | 280 | 2012 |
| 54 | 2012Q4 | Job Creation | 4380 | 610 | 440 | 60 | 210 | 120 | 220 | 1470 | 210 | 1210 | 340 | 2012 |
| 55 | 2013Q1 | Job Creation | 4250 | 460 | 290 | 140 | 160 | 120 | 150 | 1150 | 190 | 1010 | 260 | 2013 |
| 56 | 2013Q2 | Job Creation | 6240 | 550 | 620 | 75 | 260 | 200 | 250 | 1680 | 270 | 1240 | 390 | 2013 |
| 57 | 2013Q3 | Job Creation | 3970 | 500 | 430 | 40 | 160 | 170 | 190 | 1440 | 180 | 1020 | 330 | 2013 |
| 58 | 2013Q4 | Job Creation | 3860 | 600 | 460 | 50 | 210 | 170 | 180 | 1550 | 190 | 1090 | 310 | 2013 |
| 59 | 2014Q1 | Job Creation | 4020 | 560 | 340 | 130 | 180 | 110 | 210 | 1670 | 340 | 1380 | 340 | 2014 |
| 60 | 2014Q2 | Job Creation | 6400 | 700 | 550 | 55 | 230 | 220 | 260 | 1660 | 270 | 1200 | 400 | 2014 |
| 61 | 2014Q3 | Job Creation | 4040 | 460 | 320 | 45 | 130 | 130 | 200 | 1320 | 180 | 1010 | 330 | 2014 |
| 62 | 2014Q4 | Job Creation | 4410 | 630 | 370 | 55 | 150 | 90 | 190 | 1400 | 220 | 1120 | 310 | 2014 |
| 63 | 2015Q1 | Job Creation | 4690 | 510 | 330 | 75 | 150 | 95 | 180 | 1430 | 210 | 1100 | 370 | 2015 |
| 64 | 2015Q2 | Job Creation | 4910 | 730 | 690 | 25 | 240 | 160 | 170 | 1590 | 210 | 1430 | 350 | 2015 |
| 65 | 2015Q3 | Job Creation | 4700 | 540 | 410 | 40 | 170 | 100 | 210 | 1550 | 170 | 960 | 320 | 2015 |
| 66 | 2015Q4 | Job Creation | 4250 | 660 | 490 | 40 | 220 | 130 | 160 | 1180 | 180 | 1050 | 300 | 2015 |
| 67 | 2016Q1 | Job Creation | 4450 | 710 | 460 | 120 | 160 | 160 | 210 | 1530 | 190 | 1480 | 330 | 2016 |
| 68 | 2016Q2 | Job Creation | 5090 | 730 | 750 | 75 | 230 | 190 | 270 | 1760 | 240 | 1500 | 630 | 2016 |
| 69 | 2016Q3 | Job Creation | 4190 | 560 | 470 | 40 | 160 | 150 | 210 | 1270 | 180 | 1370 | 310 | 2016 |
| 70 | 2016Q4 | Job Creation | 4710 | 820 | 440 | 50 | 180 | 85 | 310 | 1430 | 220 | 1160 | 290 | 2016 |
| 71 | 2017Q1 | Job Creation | 5210 | 580 | 450 | 85 | 250 | 85 | 390 | 1780 | 270 | 1320 | 460 | 2017 |
| 72 | 2017Q2 | Job Creation | 7330 | 900 | 790 | 70 | 260 | 210 | 290 | 2160 | 240 | 1420 | 450 | 2017 |
| 73 | 2017Q3 | Job Creation | 4600 | 490 | 390 | 70 | 190 | 200 | 220 | 1930 | 210 | 1040 | 390 | 2017 |
| 74 | 2017Q4 | Job Creation | 4910 | 670 | 460 | 40 | 250 | 160 | 220 | 2130 | 190 | 1360 | 340 | 2017 |
| 75 | 2018Q1 | Job Creation | 5630 | 890 | 530 | 120 | 220 | 120 | 220 | 2150 | 180 | 1770 | 450 | 2018 |
| 76 | 2018Q2 | Job Creation | 5750 | 900 | 890 | 65 | 220 | 190 | 260 | 2070 | 260 | 2180 | 600 | 2018 |
| 77 | 2018Q3 | Job Creation | 4200 | 570 | 450 | 45 | 190 | 140 | 260 | 1430 | 170 | 1430 | 320 | 2018 |
| 78 | 2018Q4 | Job Creation | 7080 | 580 | 490 | 60 | 230 | 140 | 230 | 1570 | 270 | 1340 | 350 | 2018 |
| 79 | 2019Q1 | Job Creation | 4760 | 910 | 810 | 90 | 280 | 220 | 280 | 1610 | 270 | 1410 | 680 | 2019 |
| 80 | 2019Q2 | Job Creation | 5840 | 880 | 690 | 80 | 260 | 210 | 260 | 1840 | 280 | 1740 | 360 | 2019 |
| 81 | 2019Q3 | Job Creation | 4220 | 630 | 420 | 50 | 170 | 200 | 250 | 1510 | 190 | 1330 | 380 | 2019 |
| 82 | 2019Q4 | Job Creation | 4350 | 840 | 370 | 50 | 190 | 95 | 190 | 1880 | 270 | 1220 | 400 | 2019 |
| 83 | 2020Q1 | Job Creation | 7360 | 900 | 650 | 70 | 370 | 220 | 300 | 2280 | 340 | 2000 | 510 | 2020 |
| 84 | 2020Q2 | Job Creation | 5110 | 680 | 500 | 50 | 170 | 220 | 190 | 1500 | 230 | 1160 | 290 | 2020 |
| 85 | 2020Q3 | Job Creation | 3520 | 600 | 380 | 65 | 170 | 110 | 260 | 1250 | 250 | 880 | 270 | 2020 |
| 86 | 2020Q4 | Job Creation | 4080 | 620 | 420 | 65 | 230 | 130 | 240 | 1430 | 270 | 1090 | 400 | 2020 |
| 87 | 2021Q1 | Job Creation | 4160 | 870 | 820 | 55 | 250 | 150 | 220 | 1450 | 300 | 1360 | 430 | 2021 |
| 88 | 2021Q2 | Job Creation | 7920 | 1290 | 1330 | 80 | 640 | 250 | 340 | 2690 | 330 | 1730 | 490 | 2021 |
| 89 | 2021Q3 | Job Creation | 5390 | 680 | 410 | 50 | 220 | 190 | 290 | 1610 | 220 | 1420 | 380 | 2021 |
| 90 | 2021Q4 | Job Creation | 5510 | 960 | 740 | 85 | 280 | 200 | 240 | 2000 | 310 | 1670 | 480 | 2021 |
| 91 | 2022Q1 | Job Creation | 5480 | 750 | 620 | 80 | 240 | 130 | 240 | 1630 | 300 | 1600 | 460 | 2022 |
| 92 | 2022Q2 | Job Creation | 7750 | 1300 | 820 | 70 | 290 | 180 | 410 | 1990 | 330 | 1690 | 550 | 2022 |
| 93 | 2022Q3 | Job Creation | 4830 | 720 | 410 | 60 | 200 | 160 | 220 | 1550 | 260 | 1200 | 420 | 2022 |
| 94 | 2022Q4 | Job Creation | 5680 | 730 | 610 | 55 | 210 | 150 | 260 | 1600 | 270 | 1310 | 440 | 2022 |
| 96 | 1999Q3 | Job Destruction | 2510 | 330 | 220 | 35 | 190 | 75 | 150 | 1100 | 95 | 530 | 430 | 1999 |
| 97 | 1999Q4 | Job Destruction | 2000 | 390 | 210 | 25 | 85 | 70 | 150 | 1030 | 70 | 460 | 140 | 1999 |
| 98 | 2000Q1 | Job Destruction | 5060 | 580 | 290 | 40 | 210 | 140 | 250 | 1880 | 160 | 970 | 400 | 2000 |
| 99 | 2000Q2 | Job Destruction | 2680 | 410 | 360 | 50 | 200 | 200 | 240 | 1340 | 130 | 790 | 240 | 2000 |
| 100 | 2000Q3 | Job Destruction | 2600 | 470 | 230 | 30 | 210 | 110 | 180 | 1110 | 110 | 740 | 280 | 2000 |
| 101 | 2000Q4 | Job Destruction | 2860 | 340 | 250 | 25 | 130 | 90 | 260 | 1020 | 110 | 540 | 150 | 2000 |
| 102 | 2001Q1 | Job Destruction | 3820 | 420 | 250 | 25 | 180 | 120 | 330 | 1570 | 170 | 930 | 270 | 2001 |
| 103 | 2001Q2 | Job Destruction | 3680 | 410 | 210 | 50 | 250 | 85 | 310 | 2350 | 140 | 1720 | 180 | 2001 |
| 104 | 2001Q3 | Job Destruction | 2660 | 300 | 220 | 20 | 190 | 95 | 170 | 1290 | 90 | 720 | 250 | 2001 |
| 105 | 2001Q4 | Job Destruction | 2250 | 330 | 260 | 25 | 110 | 150 | 170 | 970 | 110 | 640 | 170 | 2001 |
| 106 | 2002Q1 | Job Destruction | 4690 | 510 | 430 | 40 | 140 | 210 | 180 | 2100 | 120 | 1000 | 230 | 2002 |
| 107 | 2002Q2 | Job Destruction | 2770 | 430 | 310 | 50 | 260 | 110 | 210 | 1700 | 170 | 780 | 210 | 2002 |
| 108 | 2002Q3 | Job Destruction | 3080 | 390 | 600 | 45 | 360 | 100 | 170 | 1290 | 110 | 680 | 180 | 2002 |
| 109 | 2002Q4 | Job Destruction | 2220 | 310 | 320 | 30 | 130 | 60 | 150 | 880 | 100 | 500 | 170 | 2002 |
| 110 | 2003Q1 | Job Destruction | 3340 | 350 | 280 | 45 | 230 | 85 | 220 | 1460 | 130 | 630 | 190 | 2003 |
| 111 | 2003Q2 | Job Destruction | 3290 | 380 | 270 | 50 | 220 | 160 | 220 | 1620 | 150 | 750 | 180 | 2003 |
| 112 | 2003Q3 | Job Destruction | 2720 | 350 | 690 | 30 | 240 | 75 | 180 | 940 | 120 | 580 | 160 | 2003 |
| 113 | 2003Q4 | Job Destruction | 2960 | 340 | 440 | 20 | 190 | 110 | 150 | 990 | 130 | 520 | 210 | 2003 |
| 114 | 2004Q1 | Job Destruction | 3130 | 540 | 370 | 50 | 140 | 140 | 210 | 1060 | 140 | 730 | 250 | 2004 |
| 115 | 2004Q2 | Job Destruction | 3470 | 460 | 340 | 40 | 190 | 140 | 220 | 1570 | 170 | 600 | 240 | 2004 |
| 116 | 2004Q3 | Job Destruction | 2700 | 310 | 330 | 30 | 210 | 130 | 180 | 910 | 100 | 600 | 210 | 2004 |
| 117 | 2004Q4 | Job Destruction | 2390 | 420 | 240 | 30 | 120 | 170 | 170 | 830 | 95 | 500 | 170 | 2004 |
| 118 | 2005Q1 | Job Destruction | 3330 | 530 | 470 | 45 | 150 | 130 | 250 | 1050 | 140 | 840 | 240 | 2005 |
| 119 | 2005Q2 | Job Destruction | 3740 | 520 | 360 | 65 | 180 | 190 | 240 | 1180 | 180 | 800 | 240 | 2005 |
| 120 | 2005Q3 | Job Destruction | 2580 | 440 | 420 | 25 | 310 | 160 | 260 | 1240 | 130 | 800 | 190 | 2005 |
| 121 | 2005Q4 | Job Destruction | 2440 | 360 | 240 | 40 | 130 | 75 | 180 | 780 | 130 | 560 | 270 | 2005 |
| 122 | 2006Q1 | Job Destruction | 4060 | 700 | 350 | 60 | 220 | 190 | 280 | 1600 | 170 | 920 | 250 | 2006 |
| 123 | 2006Q2 | Job Destruction | 4770 | 520 | 330 | 60 | 230 | 120 | 580 | 1230 | 180 | 1300 | 310 | 2006 |
| 124 | 2006Q3 | Job Destruction | 2620 | 550 | 400 | 25 | 190 | 150 | 170 | 1030 | 170 | 660 | 180 | 2006 |
| 125 | 2006Q4 | Job Destruction | 3300 | 510 | 300 | 35 | 160 | 150 | 480 | 1050 | 140 | 760 | 320 | 2006 |
| 126 | 2007Q1 | Job Destruction | 4240 | 670 | 370 | 70 | 200 | 110 | 250 | 1440 | 200 | 930 | 330 | 2007 |
| 127 | 2007Q2 | Job Destruction | 3490 | 480 | 410 | 30 | 200 | 150 | 300 | 1440 | 270 | 910 | 220 | 2007 |
| 128 | 2007Q3 | Job Destruction | 3440 | 380 | 460 | 35 | 210 | 170 | 180 | 1150 | 150 | 860 | 250 | 2007 |
| 129 | 2007Q4 | Job Destruction | 2950 | 370 | 360 | 80 | 110 | 120 | 180 | 880 | 160 | 760 | 220 | 2007 |
| 130 | 2008Q1 | Job Destruction | 4150 | 720 | 380 | 65 | 240 | 110 | 290 | 1670 | 290 | 960 | 540 | 2008 |
| 131 | 2008Q2 | Job Destruction | 3570 | 460 | 490 | 65 | 240 | 170 | 290 | 1360 | 240 | 1070 | 350 | 2008 |
| 132 | 2008Q3 | Job Destruction | 3510 | 510 | 730 | 25 | 260 | 110 | 200 | 1050 | 200 | 810 | 280 | 2008 |
| 133 | 2008Q4 | Job Destruction | 3600 | 510 | 340 | 45 | 150 | 150 | 260 | 1120 | 160 | 730 | 250 | 2008 |
| 134 | 2009Q1 | Job Destruction | 5070 | 1010 | 740 | 55 | 160 | 150 | 250 | 1670 | 220 | 1150 | 590 | 2009 |
| 135 | 2009Q2 | Job Destruction | 5000 | 610 | 310 | 85 | 250 | 150 | 220 | 1280 | 350 | 910 | 280 | 2009 |
| 136 | 2009Q3 | Job Destruction | 3840 | 510 | 590 | 40 | 200 | 140 | 250 | 1210 | 180 | 810 | 310 | 2009 |
| 137 | 2009Q4 | Job Destruction | 3600 | 360 | 260 | 35 | 120 | 120 | 160 | 910 | 150 | 630 | 200 | 2009 |
| 138 | 2010Q1 | Job Destruction | 4290 | 680 | 350 | 45 | 180 | 190 | 260 | 1580 | 310 | 970 | 380 | 2010 |
| 139 | 2010Q2 | Job Destruction | 4960 | 570 | 320 | 65 | 190 | 130 | 380 | 1860 | 210 | 1140 | 310 | 2010 |
| 140 | 2010Q3 | Job Destruction | 3360 | 590 | 520 | 50 | 320 | 120 | 180 | 1040 | 180 | 740 | 390 | 2010 |
| 141 | 2010Q4 | Job Destruction | 2770 | 430 | 240 | 35 | 160 | 75 | 160 | 810 | 200 | 620 | 220 | 2010 |
| 142 | 2011Q1 | Job Destruction | 4440 | 710 | 400 | 65 | 150 | 110 | 290 | 1400 | 180 | 1040 | 320 | 2011 |
| 143 | 2011Q2 | Job Destruction | 3250 | 520 | 290 | 35 | 250 | 170 | 220 | 1590 | 240 | 1250 | 210 | 2011 |
| 144 | 2011Q3 | Job Destruction | 4130 | 340 | 450 | 40 | 160 | 140 | 150 | 1120 | 150 | 750 | 290 | 2011 |
| 145 | 2011Q4 | Job Destruction | 2720 | 350 | 230 | 50 | 130 | 70 | 230 | 1010 | 190 | 690 | 290 | 2011 |
| 146 | 2012Q1 | Job Destruction | 4430 | 930 | 380 | 60 | 230 | 160 | 340 | 1470 | 190 | 1160 | 280 | 2012 |
| 147 | 2012Q2 | Job Destruction | 3920 | 500 | 270 | 45 | 230 | 100 | 230 | 980 | 200 | 1130 | 270 | 2012 |
| 148 | 2012Q3 | Job Destruction | 2880 | 350 | 480 | 20 | 270 | 90 | 180 | 990 | 240 | 720 | 380 | 2012 |
| 149 | 2012Q4 | Job Destruction | 3180 | 510 | 380 | 35 | 190 | 100 | 190 | 1640 | 160 | 700 | 270 | 2012 |
| 150 | 2013Q1 | Job Destruction | 3790 | 600 | 430 | 90 | 160 | 120 | 210 | 1240 | 260 | 1180 | 290 | 2013 |
| 151 | 2013Q2 | Job Destruction | 3780 | 480 | 280 | 100 | 140 | 100 | 180 | 990 | 180 | 900 | 260 | 2013 |
| 152 | 2013Q3 | Job Destruction | 3140 | 460 | 440 | 30 | 240 | 110 | 220 | 1380 | 150 | 950 | 220 | 2013 |
| 153 | 2013Q4 | Job Destruction | 2630 | 410 | 320 | 25 | 170 | 150 | 170 | 1110 | 170 | 740 | 300 | 2013 |
| 154 | 2014Q1 | Job Destruction | 5840 | 1030 | 540 | 65 | 200 | 140 | 210 | 1660 | 190 | 1040 | 400 | 2014 |
| 155 | 2014Q2 | Job Destruction | 3510 | 550 | 360 | 110 | 180 | 120 | 260 | 1080 | 190 | 910 | 300 | 2014 |
| 156 | 2014Q3 | Job Destruction | 3260 | 420 | 460 | 35 | 180 | 140 | 170 | 1060 | 180 | 870 | 220 | 2014 |
| 157 | 2014Q4 | Job Destruction | 3330 | 400 | 260 | 35 | 160 | 200 | 160 | 880 | 130 | 780 | 260 | 2014 |
| 158 | 2015Q1 | Job Destruction | 4380 | 960 | 300 | 35 | 220 | 120 | 240 | 1500 | 320 | 1190 | 310 | 2015 |
| 159 | 2015Q2 | Job Destruction | 5150 | 540 | 340 | 70 | 140 | 150 | 210 | 1180 | 210 | 1120 | 340 | 2015 |
| 160 | 2015Q3 | Job Destruction | 3850 | 500 | 470 | 70 | 260 | 160 | 160 | 1190 | 290 | 950 | 300 | 2015 |
| 161 | 2015Q4 | Job Destruction | 2820 | 510 | 280 | 30 | 180 | 120 | 170 | 1050 | 150 | 700 | 290 | 2015 |
| 162 | 2016Q1 | Job Destruction | 4130 | 780 | 410 | 80 | 260 | 140 | 230 | 1530 | 330 | 1270 | 500 | 2016 |
| 163 | 2016Q2 | Job Destruction | 4090 | 520 | 300 | 130 | 130 | 180 | 210 | 1680 | 200 | 1030 | 250 | 2016 |
| 164 | 2016Q3 | Job Destruction | 3520 | 440 | 640 | 45 | 180 | 180 | 180 | 1100 | 180 | 930 | 250 | 2016 |
| 165 | 2016Q4 | Job Destruction | 3180 | 390 | 870 | 30 | 130 | 100 | 180 | 1040 | 170 | 1040 | 270 | 2016 |
| 166 | 2017Q1 | Job Destruction | 3930 | 740 | 380 | 60 | 160 | 130 | 230 | 1950 | 180 | 2280 | 300 | 2017 |
| 167 | 2017Q2 | Job Destruction | 4100 | 620 | 460 | 95 | 180 | 190 | 340 | 1550 | 160 | 1040 | 380 | 2017 |
| 168 | 2017Q3 | Job Destruction | 3930 | 510 | 600 | 55 | 190 | 160 | 140 | 1560 | 160 | 860 | 230 | 2017 |
| 169 | 2017Q4 | Job Destruction | 3290 | 390 | 270 | 50 | 180 | 110 | 150 | 1590 | 130 | 690 | 290 | 2017 |
| 170 | 2018Q1 | Job Destruction | 4470 | 860 | 510 | 55 | 210 | 110 | 390 | 2120 | 240 | 1840 | 440 | 2018 |
| 171 | 2018Q2 | Job Destruction | 4610 | 770 | 470 | 120 | 170 | 95 | 320 | 1570 | 180 | 1310 | 410 | 2018 |
| 172 | 2018Q3 | Job Destruction | 4720 | 550 | 720 | 12 | 200 | 120 | 220 | 1480 | 210 | 1390 | 360 | 2018 |
| 173 | 2018Q4 | Job Destruction | 3580 | 500 | 360 | 30 | 150 | 160 | 220 | 1100 | 140 | 990 | 350 | 2018 |
| 174 | 2019Q1 | Job Destruction | 5350 | 620 | 570 | 55 | 220 | 130 | 240 | 1770 | 270 | 1290 | 390 | 2019 |
| 175 | 2019Q2 | Job Destruction | 4690 | 660 | 430 | 75 | 270 | 160 | 260 | 1930 | 210 | 1430 | 470 | 2019 |
| 176 | 2019Q3 | Job Destruction | 4680 | 680 | 680 | 50 | 240 | 160 | 160 | 1170 | 210 | 940 | 340 | 2019 |
| 177 | 2019Q4 | Job Destruction | 5480 | 640 | 450 | 45 | 190 | 240 | 320 | 1820 | 230 | 1200 | 430 | 2019 |
| 178 | 2020Q1 | Job Destruction | 5010 | 630 | 470 | 40 | 210 | 120 | 310 | 1320 | 250 | 1250 | 410 | 2020 |
| 179 | 2020Q2 | Job Destruction | 4520 | 760 | 520 | 70 | 180 | 150 | 280 | 1600 | 310 | 1440 | 440 | 2020 |
| 180 | 2020Q3 | Job Destruction | 4190 | 580 | 400 | 25 | 210 | 160 | 200 | 1120 | 210 | 1200 | 280 | 2020 |
| 181 | 2020Q4 | Job Destruction | 3470 | 430 | 350 | 40 | 150 | 140 | 220 | 1130 | 230 | 850 | 280 | 2020 |
| 182 | 2021Q1 | Job Destruction | 5100 | 660 | 330 | 55 | 200 | 210 | 200 | 1450 | 230 | 1080 | 350 | 2021 |
| 183 | 2021Q2 | Job Destruction | 4950 | 920 | 690 | 45 | 250 | 180 | 260 | 2130 | 230 | 1870 | 450 | 2021 |
| 184 | 2021Q3 | Job Destruction | 3060 | 660 | 630 | 50 | 170 | 170 | 260 | 920 | 210 | 970 | 310 | 2021 |
| 185 | 2021Q4 | Job Destruction | 3340 | 560 | 690 | 35 | 150 | 100 | 210 | 970 | 190 | 1030 | 280 | 2021 |
| 186 | 2022Q1 | Job Destruction | 3950 | 700 | 450 | 55 | 190 | 170 | 170 | 1500 | 380 | 1110 | 380 | 2022 |
| 187 | 2022Q2 | Job Destruction | 4450 | 810 | 490 | 75 | 230 | 140 | 280 | 1970 | 310 | 1090 | 390 | 2022 |
| 188 | 2022Q3 | Job Destruction | 4940 | 690 | 670 | 85 | 210 | 120 | 210 | 1180 | 230 | 1020 | 380 | 2022 |
| 189 | 2022Q4 | Job Destruction | 4100 | 520 | 460 | 130 | 140 | 140 | 230 | 1020 | 190 | 880 | 360 | 2022 |
grouped_auckland = regionjob.groupby(['Auckland','Type'])
grouped_auckland.groups
{(2000, 'Job Destruction'): [97], (2220, 'Job Destruction'): [109], (2250, 'Job Destruction'): [105], (2390, 'Job Destruction'): [117], (2440, 'Job Destruction'): [121], (2510, 'Job Destruction'): [96], (2580, 'Job Destruction'): [120], (2600, 'Job Destruction'): [100], (2610, 'Job Creation'): [15], (2620, 'Job Destruction'): [124], (2630, 'Job Destruction'): [153], (2660, 'Job Destruction'): [104], (2680, 'Job Destruction'): [99], (2700, 'Job Destruction'): [116], (2710, 'Job Creation'): [1], (2720, 'Job Destruction'): [112, 145], (2770, 'Job Destruction'): [107, 141], (2820, 'Job Destruction'): [161], (2860, 'Job Destruction'): [101], (2870, 'Job Creation'): [18], (2880, 'Job Destruction'): [148], (2950, 'Job Destruction'): [129], (2960, 'Job Destruction'): [113], (3010, 'Job Creation'): [19], (3040, 'Job Creation'): [2], (3060, 'Job Creation'): [6, 10, 42], (3060, 'Job Destruction'): [184], (3080, 'Job Destruction'): [108], (3090, 'Job Creation'): [53], (3130, 'Job Creation'): [12], (3130, 'Job Destruction'): [114], (3140, 'Job Destruction'): [152], (3150, 'Job Creation'): [41], (3180, 'Job Destruction'): [149, 165], (3250, 'Job Destruction'): [143], (3260, 'Job Creation'): [37], (3260, 'Job Destruction'): [156], (3280, 'Job Creation'): [9], (3290, 'Job Creation'): [21], (3290, 'Job Destruction'): [111, 169], (3300, 'Job Destruction'): [125], (3330, 'Job Creation'): [50], (3330, 'Job Destruction'): [118, 157], (3340, 'Job Creation'): [3, 5], (3340, 'Job Destruction'): [110, 185], (3360, 'Job Creation'): [38, 39], (3360, 'Job Destruction'): [140], (3370, 'Job Creation'): [22], (3420, 'Job Creation'): [30], (3440, 'Job Destruction'): [128], (3450, 'Job Creation'): [46], (3470, 'Job Destruction'): [115, 181], (3490, 'Job Creation'): [14], (3490, 'Job Destruction'): [127], (3510, 'Job Destruction'): [132, 155], (3520, 'Job Creation'): [85], (3520, 'Job Destruction'): [164], (3570, 'Job Destruction'): [131], (3580, 'Job Destruction'): [173], (3590, 'Job Creation'): [49], (3600, 'Job Destruction'): [133, 137], (3680, 'Job Creation'): [13], (3680, 'Job Destruction'): [103], (3700, 'Job Creation'): [4, 23, 31], (3740, 'Job Destruction'): [119], (3780, 'Job Destruction'): [151], (3790, 'Job Destruction'): [150], (3810, 'Job Creation'): [26], (3820, 'Job Creation'): [40], (3820, 'Job Destruction'): [102], (3840, 'Job Destruction'): [136], (3850, 'Job Destruction'): [160], (3860, 'Job Creation'): [58], (3880, 'Job Creation'): [25], (3890, 'Job Creation'): [35], (3920, 'Job Creation'): [17, 27], (3920, 'Job Destruction'): [147], (3930, 'Job Destruction'): [166, 168], (3950, 'Job Destruction'): [186], (3970, 'Job Creation'): [57], (3980, 'Job Creation'): [33], (4000, 'Job Creation'): [8], (4020, 'Job Creation'): [59], (4030, 'Job Creation'): [29], (4040, 'Job Creation'): [61], (4060, 'Job Destruction'): [122], (4080, 'Job Creation'): [86], (4090, 'Job Destruction'): [163], (4100, 'Job Destruction'): [167, 189], (4130, 'Job Destruction'): [144, 162], (4150, 'Job Destruction'): [130], (4160, 'Job Creation'): [87], (4190, 'Job Creation'): [69], (4190, 'Job Destruction'): [180], (4200, 'Job Creation'): [24, 77], (4220, 'Job Creation'): [81], (4240, 'Job Destruction'): [126], (4250, 'Job Creation'): [55, 66], (4290, 'Job Destruction'): [138], (4350, 'Job Creation'): [82], ...}
grouped_auckland.groups[3290, 'Job Destruction']
Index([111, 169], dtype='int64')
regionjob1=regionjob.pivot_table(values=['Auckland'], index=['Type'], columns='Year')
regionjob1
| Auckland | ||||||||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Year | 1999 | 2000 | 2001 | 2002 | 2003 | 2004 | 2005 | 2006 | 2007 | 2008 | 2009 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2018 | 2019 | 2020 | 2021 | 2022 |
| Type | ||||||||||||||||||||||||
| Job Creation | 2875.0 | 3360.0 | 3775.0 | 3680.0 | 3647.5 | 3632.5 | 3897.5 | 4107.5 | 4345.0 | 3985.0 | 3347.5 | 4532.5 | 4327.5 | 4315.0 | 4580.0 | 4717.5 | 4637.5 | 4610.0 | 5512.5 | 5665.0 | 4792.5 | 5017.5 | 5745.0 | 5935.0 |
| Job Destruction | 2255.0 | 3300.0 | 3102.5 | 3190.0 | 3077.5 | 2922.5 | 3022.5 | 3687.5 | 3530.0 | 3707.5 | 4377.5 | 3845.0 | 3635.0 | 3602.5 | 3335.0 | 3985.0 | 4050.0 | 3730.0 | 3812.5 | 4345.0 | 5050.0 | 4297.5 | 4112.5 | 4360.0 |
3.6.2 Visualize the pivot table.ΒΆ
years = regionjob1.columns.levels[1].tolist()
job_creation = regionjob1.loc['Job Creation'].values.flatten().tolist()
job_destruction = regionjob1.loc['Job Destruction'].values.flatten().tolist()
plt.figure(figsize=(12, 6))
plt.plot(years, job_creation, label='Job Creation', marker='o')
plt.plot(years, job_destruction, label='Job Destruction', marker='x')
plt.title('Job Creation and Destruction in Auckland from 1999 to 2022')
plt.xlabel('Year')
plt.ylabel('Number of Jobs')
plt.xticks(years, rotation=45)
plt.legend()
<matplotlib.legend.Legend at 0x2ca66e42050>
Based on the table, we can see that from 1999 to 2022, the number of jobs created in Auckland is mostly greater than the number of jobs destroyed, indicating a generally positive trend in the job market. However, there are two points in time, 1999 and 2019, where job destruction exceeded job creation. This could be due to specific policies implemented in Auckland or the impact of the COVID-19 pandemic causing these changes.
3.7 What are the Employment Prospects and Highest Salary Ranges for the Manufacturing and Technology Industries, as well as for Data Analyst Positions?ΒΆ
3.7.1 Group by the Manufacturing and technology industry with Good Job Opportunities.ΒΆ
df_jobprofiles
| Occupation | Sub Title | Description | Job Opportunities | Training Required | Industry | Earnings | |
|---|---|---|---|---|---|---|---|
| 0 | Energy/βCarbon Auditor | KaitΔtari PΕ«ngao/βWaro | Energy/carbon auditors assess the amount of energy used and carbon produced by organisations. They also recommend ways to increase energy efficiency. | Average | 2-3 years | Construction and infrastructure, Services industries | 80K per year, 200K per year |
| 1 | Television Presenter | KaipΔnui Pouaka Whakaata | Television presenters introduce, present or host programmes on television. | Poor | N/A | Creative industries | |
| 2 | Facilities Manager | Kaiwhakahaere Whakaurunga | Facilities managers co-ordinate the strategic and operational management of buildings and facilities to ensure they are safe, healthy, sustainable, productive and fit-for-purpose. | Good | N/A | Construction and infrastructure | 100K per year, 150K per year |
| 3 | Aircraft Refueller | KaiwhakakΔ« Waka Rererangi | Aircraft refuellers fill aircraft with fuel at airports. | Poor | <1 year | Services industries | 65K per year, 75K per year |
| 4 | Electronics Engineer | Mataaro TΔhiko | Electronics engineers design and oversee production of electronic equipment such as radios, televisions, computers, washing machines and telecommunication systems. They may also work in sales and technical support. | Good | 4 years | Manufacturing and technology | $100K per year |
| 5 | Dairy Processing Operator | Kaiwhakamahi PΕ«rere Hua Miraka | Dairy processing operators oversee the operation of equipment used to produce a wide range of dairy products such as cheese, butter, yoghurt and milk powder. | Good | N/A | Manufacturing and technology, Primary industries | 60K per year, 80K per year |
| 6 | Trainer | Kaiwhakangungu/βKaiwhakaako | Trainers plan and provide training courses for employees of businesses, government and other organisations. | Good | N/A | Services industries | 82K per year, 128K per year |
| 7 | Mortgage Broker | Kaitakawaenga PΕ«tea Taurewa | Mortgage brokers offer financial advice to people wanting to buy a house or property and help them apply for a home loan. | Good | <1 year | Services industries | 100K per year |
| 8 | Insurance Loss Adjuster | Kaiwhakatika Makeretanga RΔ«anga | Insurance loss adjusters investigate and calculate insurance claims. | Average | 0-3 years | Services industries | 88K per year, 143K per year |
| 9 | Agricultural/βHorticultural Field Representative | MΔngai Taiao Ahuwhenua | Agricultural/horticultural field representatives sell products such as farm equipment, and advise clients on crop and livestock management. | Good | N/A | Primary industries | 90K per year, 120K per year |
| 10 | Statistician | Kaitatau | Statisticians design studies and surveys, and collect, analyse, interpret and present numerical information to assist in decision-making. | Good | 4 years | Services industries, Social and community services | 65K per year, 140K per year |
| 11 | Artistic Director | KaihautΕ« Toi | Artistic directors plan and direct the activities of performing arts organisations such as theatre and dance companies, and arts activities at festivals and venues. | Poor | N/A | Creative industries | |
| 12 | Director (Film, Television, Radio or Stage) | Kaitohu (Kiriata, Pouaka Whakaata, Irirangi, Whakaari rΔnei) | Directors instruct cast and crew and oversee the artistic and production aspects of film, television, radio or stage creations. | Poor | N/A | Creative industries | |
| 13 | Forestry Scientist | KaipΕ«taiao Ngahere | Forestry scientists research forest growth, wood processing, conservation and different types of trees, and how these can be used. | Good | 3-4 years | Primary industries | 98K per year, 150K per year |
| 14 | Tattoo Artist | Ringa Kirituhi | Tattoo artists use sterilised skin-piercing equipment and ink or jewellery to decorate people's skin. | Poor | <1 year | Creative industries | |
| 15 | Minister of Religion | Amorangi | Ministers of religion provide leadership, guidance and training for members of a religious group. | Poor | N/A | Social and community services | |
| 16 | Energy and Chemical Plant Operator | Kaiwhakahaere Rawa PΕ«ngao, Rawa MatΕ« | Energy and chemical plant operators monitor, control and maintain machinery and equipment at industrial sites such as power stations. | Average | N/A | Manufacturing and technology | 80K per year, 180K per year |
| 17 | Forest Manager | Kaimahi Ngahere | Forest managers plan and direct the planting, growth, harvesting and protection of forests for wood production. | Good | 3 years | Primary industries | 95K per year, 150K per year |
| 18 | Epidemiologist | KaimΔtai Tahumaero | Epidemiologists study the causes, transmission and distribution of diseases in population groups to inform public health programmes and prevent the spread of disease. | Good | 5 years | Social and community services | 86K per year, 175K per year |
| 19 | Wool Classer | KaimΔhiti WΕ«ru | Wool classers sort wool into categories. They ensure wool is clean, identified and documented for sale. | Average | N/A | Primary industries | 60 per hour |
| 20 | Agricultural/βHorticultural Scientist | KaipΕ«taiao Ahuwhenua | Agricultural/horticultural scientists study farm animals, soils, pastures and crops to improve growth, health and quality, and to prevent pests and disease. | Good | 5-9 years | Primary industries | 75K per year, 150K per year |
| 21 | Geophysicist | KaimΔtai PΕ«taiao Whenua | Geophysicists use data-collecting technology to study natural processes of the Earth, such as earthquake and volcanic activity, and to locate minerals, oil and gas, or groundwater. | Average | 3-7 years | Primary industries, Services industries | 75K per year, 180K per year |
| 22 | Workplace Relations Adviser | Kaitohutohu Takawaenga Mahi | Workplace relations advisers provide advice and mediation to different groups in the workplace to prevent and resolve workplace disputes. | Average | 2-3 years | Services industries | 122K per year, 163K per year |
| 23 | Telecommunications Engineer | Mataaro Whitiwhiti KΕrero | Telecommunications engineers design, test and build telecommunications networks and systems. | Good | 2-4 years | Manufacturing and technology | 65K per year, 140K per year |
| 24 | Valuer | Kaiwhakatau WΔriu | Valuers assess the value of real estate or personal property such as art and jewellery, for sales, rentals, mortgages, insurance or rates. | Good | 1-4 years | Services industries | 87K per year, 138K per year |
| 25 | Auctioneer | MΔngai Hokohoko | Auctioneers take charge of public or private auctions. They sell goods, property or livestock on behalf of the owner to people offering the highest price. | Poor | N/A | Services industries | |
| 26 | Information Technology Manager | Kaiwhakahaere Hangarau PΔrongo | Information technology (IT) managers plan and supervise computer and information technology services for organisations or technical teams. | Good | 2-3 years | Manufacturing and technology, Services industries | 190K per year, 350K per year |
| 27 | Judge | KaiwhakawΔ | Judges listen to court cases and make decisions on matters of law. | Poor | 11 years | Social and community services | 578K per year |
| 28 | Chemical Engineer | Mataaro MatΕ« | Chemical engineers design, develop and operate equipment and processes used to manufacture chemicals and products. | Good | 3-4 years | Manufacturing and technology | $100K per year |
| 29 | Curator | Kaitiaki Taonga | Curators research, develop, exhibit and maintain collections for museums, art galleries and artists. | Poor | 5 years | Social and community services, Services industries, Creative industries | 65K per year, 95K per year |
| 30 | Tertiary Lecturer | PΕ«kenga Whare WΔnanga | Tertiary lecturers teach at universities, Te PΕ«kenga, wΔnanga and other post-secondary education providers. They also carry out research and do administrative tasks. | Good | 1-8 years | Social and community services | 108K per year, 213K per year |
| 31 | Environmental Scientist | KaipΕ«taiao Ao TΕ«roa | Environmental scientists study human effects on the environment such as climate change, pollution and loss of biodiversity. They also advise on how to avoid or reduce these harmful effects. | Good | 3-9 years | Primary industries, Social and community services | 165K per year |
| 32 | Wall and Floor Tiler | Kaiwhakatakoto Taera Pakitara, Taera Papa | Wall and floor tilers lay ceramic, clay, slate, marble and glass tiles. | Good | N/A | Construction and infrastructure | 25 per hour, 35 per hour |
| 33 | Watchmaker and Repairer | Kaihanga/βKaiwhakatika Karaka/βMatawΔ | Watchmakers and repairers clean, repair and assemble mechanical or electronic timepieces such as watches and clocks. | Poor | 4 years | Manufacturing and technology, Services industries | 55K per year, $80K per year |
| 34 | Acupuncturist | Kaiwero Ngira Hauora | Acupuncturists give general health advice and treat patients using therapies such as electronic and needle acupuncture, cupping, skin scraping (gua sha), the heating of acupuncture points (moxibustion) and tuina (massage). | Average | 4 years | Social and community services | 100K per year |
| 35 | Vehicle Groomer/βCleaner | Kaiwhakapaipai Waka | Vehicle groomers/cleaners clean and polish vehicles, and may also drive, park and maintain vehicles. | Good | N/A | Services industries | $23 per hour |
| 36 | Brick and Blocklayer | Ringa Tiri Pereki/βRinga Tiri Poraka | Brick and blocklayers lay bricks, concrete blocks and tiles to construct or repair buildings, walls, arches, chimneys or paved areas. | Good | 2-3 years | Construction and infrastructure | 25 per hour, 60 per hour |
| 37 | Building Insulator | KaitauΔrai Whare | Building insulators install or apply special material to buildings or equipment to prevent or reduce heat, cold, air, sound or moisture loss. | Good | <1 year | Construction and infrastructure | 24 per hour, 30 per hour |
| 38 | Carpet Cleaner | Kaiwhakapai WhΔriki | Carpet cleaners clean carpets, floors and upholstery, and may dry and clean carpets after floods. | Good | <1 year | Services industries | 30 per hour |
| 39 | Textile Process Operator | Kaimahi Kaka-aku | Textile process operators carry out a variety of tasks in the production of materials such as fabric, canvas, yarn and carpet. | Poor | <1 year | Manufacturing and technology | 24 per hour |
| 40 | Cleaner | Kaihoroi Whare | Cleaners clean offices, factories, shops, public buildings, schools, private homes and aircraft. | Good | N/A | Services industries | 24 per hour |
| 41 | Tailor/βDressmaker | Kaihanga KΔkahu | Tailors/dressmakers design, make, alter and repair clothing. | Poor | N/A | Services industries, Creative industries | $23 per hour |
| 42 | Taxi Driver/βChauffeur | Kaitaraiwa Waka PΔhihi | Taxi drivers/chauffeurs drive vehicles to transport passengers from one place to another. | Good | N/A | Services industries | 25 per hour |
| 43 | Street/βPark Cleaner | Kaitahitahi Papa RΔhia/βHuarahi | Street/park cleaners clean and maintain public areas such as streets, parks and buildings. | Poor | N/A | Services industries | 26 per hour |
| 44 | Service Station Attendant | Kaihoko Penehini/βHinu | Service station attendants help customers get petrol, gas or oil for their vehicle, and sell motoring accessories and food items. | Average | N/A | Services industries | $23 per hour |
| 45 | Roofer | Kaihanga Tuanui | Roofers repair or install roofs using materials such as roofing iron, tiles and shingles. | Good | 3 years | Construction and infrastructure | 30 per hour, 40 per hour |
| 46 | Concrete Worker | Kaimahi Raima | Concrete workers make, pour, spread, finish, reinforce and cut concrete for construction projects such as buildings and footpaths. | Good | N/A | Construction and infrastructure | 30 per hour |
| 47 | Plastics Technician | Kaihangarau Kirihou | Plastics technicians set up, adjust, maintain and repair machines that manufacture plastic products. | Good | 3 years | Manufacturing and technology | 25 per hour, 34 per hour |
| 48 | Plastics Worker | Kaimahi Kirihou | Plastics workers operate the machinery that makes, assembles and repairs plastic, composite and rubber products. | Average | <1 year | Manufacturing and technology | 25 per hour |
| 49 | Patternmaker | Ringa Tauira KΔkahu | Patternmakers turn clothing designs into patterns. | Average | 1-2 years | Manufacturing and technology, Creative industries | 36 per hour |
| 50 | Plasterer | Kaiwhakapiri Uhi | Plasterers apply plaster or other materials to buildings. They usually specialise in either interior or exterior plastering. | Good | 1-3 years | Construction and infrastructure | 30 per hour |
| 51 | Photographer | Kaitango Whakaahua | Photographers take photographs of people, places, products or events. | Average | N/A | Creative industries | 35 per hour, 250 per hour |
| 52 | Fencer | Kaihanga Taiapa | Fencers construct and repair fences, barriers or walls made of timber, metal, wire, chain-link or other materials. | Good | N/A | Primary industries, Construction and infrastructure | 30 per hour, 62 per hour |
| 53 | Mail and Parcel Sorter | KaimΔhiti Reta/βPΕ«hera | Mail and parcel sorters work in processing centres. They sort mail and parcels by address, either manually or by machine. | Average | <1 year | Services industries | $23 per hour |
| 54 | Locksmith | Kaimahi Raka | Locksmiths install, maintain and replace locks, keys, safes, electronic locking devices and access control systems for buildings and vehicles. They may also install and repair security systems. | Average | 3 years | Construction and infrastructure, Services industries, Manufacturing and technology | 25 per hour, 29 per hour |
| 55 | Flooring Installer | Kaiwhakauru Uhi | Flooring installers lay, replace and repair floor coverings such as carpet, linoleum, vinyl and timber. | Good | 2-3 years | Construction and infrastructure | 24 per hour, 35 per hour |
| 56 | Metal Worker | Kaimahi Maitai | Metal workers make patterns and moulds for metal castings, heat and hammer metal into shape, and repair metal parts and equipment. | Poor | 4 years | Construction and infrastructure, Manufacturing and technology | 29 per hour |
| 57 | Lighting Technician | Kaihangarau Rama | Lighting technicians set up and operate lighting equipment to provide light and special lighting effects in theatres, at events, and for film and television productions. | Poor | N/A | Services industries, Creative industries | 27 per hour, 70 per hour |
| 58 | Laundry Worker/βDry-cleaner | Kaimahi Horoi KΔkahu/βKaiwhakamohani KΔkahu | Laundry workers/dry-cleaners clean, wash and care for clothing, curtains and bedding. | Good | N/A | Services industries | 30 per hour |
| 59 | Forklift Operator | Kaitaraiwa Waka Uta | Forklift operators operate vehicles that have a lifting platform for shifting and stacking heavy articles such as pallets, bales, crates, containers or cartons. | Good | <1 year | Construction and infrastructure, Manufacturing and technology | 24 per hour |
| 60 | Glass Processor | Kaiwhakarite Karaehe | Glass processors prepare and process sheets of flat glass into products such as windows and mirrors for installation in buildings and related structures. | Good | 3 years | Manufacturing and technology | 25 per hour, 36 per hour |
| 61 | Groundsperson | Kaimanaaki Papa | Groundspeople are in charge of the turf (grass), tracks and pitches at sports fields, golf courses, public areas, schools and racecourses. | Good | N/A | Primary industries | 60K per year, 100K per year |
| 62 | Glazier | Kaimahi Karaehe | Glaziers install or replace glass or mirrors in buildings, vehicles or boats and may create decorative glass features. | Good | 1-3 years | Construction and infrastructure | 30 per hour |
| 63 | Furniture Packer/βMover | Kaiuta/βKaiwhakaneke Taonga | Furniture packers/movers pack furniture and equipment and move it between households, offices and storage places. | Good | N/A | Services industries | 26 per hour |
| 64 | Importer/βExporter | Kaiwhiwhi Rawa i TΔwΔhi/βKaituku Rawa Ki TΔwΔhi | Importers/exporters plan, organise, direct and co-ordinate the operations of an importing or exporting business. | Average | N/A | Services industries, Manufacturing and technology | 75K per year |
| 65 | Fire Engineer | Mataaro Δrai Ahi | Fire engineers plan and design safety features that detect, control or reduce fire and smoke in buildings and structures. They also analyse how fire behaves and how safety features perform in fire. | Good | 6 years | Construction and infrastructure | 90K per year, 180K per year |
| 66 | Recycler/βDismantler | Kaihangarua/βKaiwetewete | Recyclers/dismantlers take apart, separate, sort and sell materials to be recycled or reused. | Good | N/A | Manufacturing and technology | 25 per hour |
| 67 | Parking Officer | Δpiha Atoato Waka | Parking officers give fines for illegal parking and vehicle offences such as unregistered cars. | Poor | N/A | Social and community services | $25 per hour |
| 68 | Optometrist | KaimΔtai Whatu | Optometrists examine clients' eyes to diagnose and provide solutions for vision problems. They also diagnose, monitor and manage eye diseases such as cataracts. | Good | 5 years | Social and community services | 114K per year, 225K per year |
| 69 | Heavy Truck Driver | Kaitaraiwa Taraka Taumaha | Heavy truck drivers drive trucks with or without trailers. They may transport materials, livestock, general freight, and hazardous substances or spread fertiliser. | Good | 1-2 years | Services industries | 35 per hour, 45 per hour |
| 70 | Tow Truck Operator | Kaitaraiwa Taraka | Tow truck operators drive and operate trucks to tow vehicles that have broken down, been damaged or illegally parked. | Good | <1 year | Services industries | 25 per hour, 35 per hour |
| 71 | Bus Driver | Kaitaraiwa Pahi | Bus drivers operate buses and drive passengers along local, chartered or intercity routes. | Good | <1 year | Services industries | 30 per hour |
| 72 | Teacher of English to Speakers of Other Languages (ESOL) | Kaiako Reo PΔkehΔ (ki te Hunga KΕrero Reo KΔ) | Teachers of English to speakers of other languages (ESOL teachers) teach people from non-English speaking backgrounds how to speak, read and write English. | Poor | 1-5 years | Social and community services | 80K per year |
| 73 | Private Teacher/βTutor | Kaiako Whaiaro | Private teachers/tutors teach a specific skill or subject to individuals or small groups of children or adults. | Average | 0-3 years | Social and community services, Services industries | 39 per hour, 80 per hour |
| 74 | Pest Control Technician | Kaihangarau Patu OrotΔ | Pest control technicians identify and remove pests, such as insects, rats and mice, from buildings and properties. | Good | 1-2 years | Services industries | 34 per hour |
| 75 | Automotive Electrician | Kaimahi Hiko Δ-Waka | Automotive electricians install, maintain and repair electrical wiring, parts and electrical and electronic systems in vehicles. | Good | 3-4 years | Manufacturing and technology | 45 per hour |
| 76 | Coachbuilder/βTrimmer | Kaihanga Pahi/βKaiwhakarΔkei Waka | Coachbuilders manufacture and assemble frames, panels and parts for vehicles such as buses and motor homes. Vehicle trimmers install and repair the upholstery of vehicles. | Good | 3 years | Manufacturing and technology | 23-$26 per hour |
| 77 | Tyre Technician | Kaiwhakamau Taea | Tyre technicians remove, repair and fit tyres for all types of vehicles. They also advise customers on different types of tyres, and check and adjust vehicle wheel alignment. | Average | N/A | Manufacturing and technology, Services industries | 26 per hour |
| 78 | Environmental Engineer | Mataaro Taiao | Environmental engineers assess and reduce the impact of engineering projects on water, soil and air. They also plan and design systems to treat and remove waste. | Good | 3-4 years | Construction and infrastructure, Primary industries, Manufacturing and technology | 140K per year |
| 79 | Make-up Artist | Kaitoi Whakapaipai Kanohi | Make-up artists apply make-up to enhance or alter people's appearances. | Good | <1 year | Services industries, Creative industries | |
| 80 | Florist | Kaihoko Putiputi | Florists sell plants and fresh flowers and use them to design and create floral arrangements. | Average | N/A | Services industries, Creative industries | 24 per hour, 28 per hour |
| 81 | Aircraft Loader | Kaiuta/βKaihoroi Waka Rererangi | Aircraft loaders load and unload aircraft, and transfer freight and baggage between airport buildings and aircraft. | Average | N/A | Services industries | 29 per hour |
| 82 | Contact Centre Worker | Kaimahi PokapΕ« WhakapΔ | Contact centre workers answer enquiries and provide or organise help for those who contact them. They may also deal with customer complaints, or sell goods or services. | Good | N/A | Services industries | 65K per year, 200K per year |
| 83 | Podiatrist | Rata Waewae | Podiatrists diagnose, treat and prevent foot and lower limb problems. | Good | 3 years | Social and community services | 70K per year, 98K per year |
| 84 | Medical Laboratory Scientist | KaipΕ«taiao Taiwhanga RongoΔ | Medical laboratory scientists carry out laboratory tests on blood, tissues and other samples taken from patients. | Good | 4 years | Social and community services, Manufacturing and technology | 73K per year, 106K per year |
| 85 | Professional Sportsperson | Kaiwhakataetae Ngaio | Professional sportspeople take part in competitive national and international sports such as rugby, football, cricket, netball, golf, tennis and horse racing. | Poor | N/A | Services industries | |
| 86 | Primary School Teacher | Kaiako Kura Tuatahi | Primary school teachers teach children between the ages of five and 13 at primary or intermediate schools. | Good | 3-4 years | Social and community services | 95K per year |
| 87 | Air Traffic Controller | Kaiwhakahaere Huarahi Rererangi | Air traffic controllers direct the safe and orderly movement of aircraft while they are flying, landing, taking off and taxiing. | Poor | 1-2 years | Services industries | 245K per year, 245K per year |
| 88 | Horse Trainer | Kaiwhakapakari HΕiho | Horse trainers train horses for racing, and are responsible for their care at a stable or race track. | Average | N/A | Primary industries | |
| 89 | Auditor | KaitΔtari Kaute | Auditors examine and report on the financial records and systems of organisations to ensure they are accurate. | Good | 3-6 years | Services industries | 92K per year, 184K per year |
| 90 | Hotel/βMotel Manager | Kaiwhakahaere HΕtera/βMΕtera | Hotel/motel managers plan, organise and control the operation of a hotel, motel or hostel, including management of staff. | Good | N/A | Services industries | 80K per year, 100K per year |
| 91 | Electrical Engineer | Mataaro PΕ«hiko | Electrical engineers design, construct and manufacture electrical systems. They also maintain, operate and manage these systems. | Good | 4 years | Manufacturing and technology | 160K per year, 210K per year |
| 92 | General Practitioner | Rata Hauora | General practitioners care for, diagnose and treat the health problems of people and families in the community. | Good | 12 years | Social and community services | $187K per year |
| 93 | Train Driver | Kaitaraiwa Rerewhenua | Train drivers drive passenger or freight trains. They may shift (shunt) carriages and wagons using trains or remote controls. | Average | 1 year | Services industries | 43 per hour, 58 per hour |
| 94 | Butcher | Ringa Tapahi MΔ«ti | Butchers cut, prepare and sell meat. | Good | 3-4 years | Manufacturing and technology, Services industries | 44 per hour |
| 95 | Network Administrator | Kaiwhakahaere Whatunga | Network administrators design, install and maintain computer hardware and software networks, from one-building LANs (local area networks) to worldwide WANs (wide area networks). | Good | 1-3 years | Manufacturing and technology | 140K per year |
| 96 | Science Technician | Kaihangarau PΕ«taiao | Science technicians help scientists carry out research, testing and experiments in areas such as chemistry, earth sciences, life sciences and physical sciences. | Good | 2-5 years | Manufacturing and technology, Primary industries | $52K per year |
| 97 | Cafe/βRestaurant Manager | Kaiwhakahaere Toa Kawhe/βWharekai | Cafe/restaurant managers are in charge of running cafes, restaurants and fast food outlets. They may also run catering businesses. | Good | N/A | Services industries | 36 per hour |
| 98 | Early Childhood Teacher | Kaiako KΕhungahunga | Early childhood teachers educate and care for young children in kindergartens, kΕhanga reo or childcare centres. KΕhanga reo kaiako also help children learn te reo MΔori and tikanga MΔori (culture and customs). | Good | 3-4 years | Social and community services | 100K per year |
| 99 | Helicopter Pilot | Kaiwhakarere Toparere | Helicopter pilots fly helicopters to transport goods, people including photographers, spread fertiliser, spray crops, lift items into difficult sites and provide air rescue and ambulance services. | Good | <2 years | Services industries | , 200K per year |
| 100 | Storeperson | Tangata Whakaputu | Storepeople receive, check, store and send out goods from a warehouse, business or organisation. | Good | N/A | Services industries | 30 per hour |
| 101 | Kaiwhakaako MΔori | Kaiwhakaako MΔori | Kaiwhakaako MΔori teach in te reo MΔori at primary and secondary schools. | Good | 3-4 years | Social and community services | 103K per year |
| 102 | Secondary School Teacher | Kaiako Kura Tuarua | Secondary school teachers plan, prepare and teach one or more subjects to students between the ages of 13 and 18. | Good | 4 years | Social and community services | 89K per year, 103K per year |
| 103 | Medical Laboratory Technician | Kaihangarau Taiwhanga RongoΔ | Medical laboratory technicians take medical samples and run tests under the supervision of scientists and pathologists. | Average | 2 years | Social and community services | 67K per year |
| 104 | Phlebotomist | Kaitiki Toto | Phlebotomists collect blood and samples from patients for laboratory testing or for blood banks. | Average | 2 years | Social and community services | 82K per year |
| 105 | Air Force Aviator | Paerata TauΔrangi | Air force aviators defend their country, keep the peace and provide disaster relief. | Average | <1 year | Services industries, Social and community services | 101K-$120K per year |
| 106 | Air Force Officer | Δpiha TauΔrangi | Air force officers plan and supervise flying missions, repair and maintenance of planes, helicopters and radio equipment. | Average | <1 year | Services industries, Social and community services | 101K-$133K per year |
| 107 | Navy Sailor | Kaumoana TauΔ Moana | Navy sailors defend their country, keep the peace, patrol borders and provide disaster relief. | Good | <1 year | Services industries, Social and community services | 101K-$120K per year |
| 108 | Navy Officer | Δpiha TauΔ Moana | Navy officers train navy sailors, manage field exercises and lead sailors in combat, peacekeeping missions, border patrols and disaster relief. | Average | <1 year | Services industries, Social and community services | 101K-$133K per year |
| 109 | Orchard Farmer/βManager | KaipΔmu Uru Hua RΔkau/βKaiwhakahaere Uru Hua RΔkau | Orchard farmers/managers plan and manage fruit and nut production in orchards. | Average | N/A | Primary industries | 110K per year, 180K per year |
| 110 | Nursery Grower/βWorker | Kaiwhakatipu/βKaimahi Otaota | Nursery growers/workers grow young plants, flowers, trees and shrubs for sale or for use in parks and gardens. | Average | N/A | Primary industries | 25 per hour, 31 per hour |
| 111 | Crop Farmer/βManager | Kaiahuwhenua Huangakai/βKaiwhakahaere Huangakai | Crop farmers/managers plan and manage plant production on farms and in vineyards and hothouses. | Good | N/A | Primary industries | 110K per year, 180K per year |
| 112 | Crop Worker | Kaimahi Huangakai | Crop workers assist with the growing and harvesting of fruit, vegetables and other produce on farms, market gardens, orchards and vineyards. | Good | N/A | Primary industries | 24 per hour, 25 per hour |
| 113 | Interpreter | Kaiwhakawhiti Reo Δ-Waha | Interpreters convert what people say from one language into another. | Average | 3 years | Social and community services | 80 per hour, 140 per hour |
| 114 | Translator | Kaiwhakawhiti Reo Δ-Tuhi | Translators convert written material from one language into another. | Poor | 3 years | Services industries, Social and community services | |
| 115 | Conservator | Kaiatawhai Whakaora Taonga | Conservators help preserve art and other important historical items by preventing deterioration and repairing damage. | Poor | 5 years | Social and community services, Services industries, Creative industries | 70K per year, 82K per year |
| 116 | Midwife | Tapuhi Δ-Whare | Midwives provide care and support to pregnant people, their partners and family/whΔnau during pregnancy, labour and birth, and for six weeks following the birth. | Good | 3-4 years | Social and community services | 111K per year, 153K per year |
| 117 | Data Analyst | KaitΔtari Raraunga | Data analysts identify and describe data trends using statistics and specialised software to help organisations achieve their business aims. | Good | 2-3 years | Manufacturing and technology | 120K per year, 170K per year |
| 118 | Personal Trainer/βExercise Professional | Kaiwhakangungu Tinana/βMahi Ngaio | Personal trainers/exercise professionals provide expertise, knowledge and structured support to improve and maintain health and wellness through physical activity. | Good | N/A | Services industries | 70 per hour, 90 per hour |
| 119 | User Experience Designer | Kaihoahoa Wheako Whakamahi | User experience (UX) designers design how products such as websites and apps look and work, based on users' needs. | Good | 1-3 years | Manufacturing and technology | 175K per year |
| 120 | Veterinarian | PΕ«kenga Hauora Kararehe | Veterinarians treat sick and injured animals, provide general animal care, and advise about health care and disease prevention for pets and farm (production) animals. | Good | 5 years | Services industries, Social and community services, Primary industries | 105K per year, 220K per year |
| 121 | Paramedic | Δpiha Whakaora | Paramedics assess and treat people who are seriously ill or injured, and transport them to hospital if necessary. | Good | 3 years | Social and community services | 92K per year, 123K per year |
| 122 | Community Development Worker | KaiΔwhina Whakawhanake Hapori | Community development workers support people to develop and implement plans to make improvements in their community. | Good | N/A | Social and community services | 80K per year |
| 123 | Information Technology Architect | Kaihoahoa Hangarau PΔrongo | Information technology (IT) architects analyse an organisation's IT needs, recommend solutions and oversee their delivery and implementation. | Good | 3-5 years | Manufacturing and technology | 200K per year |
| 124 | Project Manager | Kaiwhakahaere Kaupapa | Project managers manage the planning, resourcing, scheduling and administration of projects to deliver them on time and within budget. | Good | 2-3 years | Construction and infrastructure, Services industries, Manufacturing and technology | 140K per year, 170K per year |
| 125 | Medical Imaging Technologist | Kairahurahu Whakaahua Whakaora | Medical imaging technologists use x-ray and other imaging equipment to take images of injuries and diseases. | Good | 3-5 years | Social and community services | 84K per year, 118K per year |
| 126 | Aeronautical Engineer | Mataaro Whakahaere PΕ«kaha Rererangi | Aeronautical engineers plan and supervise the design, development and modification of all types of flight vehicles. They also monitor and analyse in-service failures and faults. | Good | 3-4 years | Manufacturing and technology, Services industries | $100K per year |
| 127 | Systems Administrator | Kaiwhakahaere PΕ«naha | Systems administrators develop, maintain and administer computer operating systems, database management systems, and security policies and procedures. | Good | 2-4 years | Manufacturing and technology | 120K per year, 145K per year |
| 128 | Boat Builder | Kaihanga Waka | Boat builders build, repair, and sometimes design boats and their interiors. This can include furnishings, engines, electrics and plumbing. | Good | 3-4 years | Construction and infrastructure, Manufacturing and technology | 30 per hour, 50 per hour |
| 129 | Naval Architect | Kaihoahoa TΔruru | Naval architects plan, design and supervise the construction and repair of ships, yachts and boats. | Good | 4 years | Construction and infrastructure, Creative industries | 78K per year, 150K per year |
| 130 | Roadmarker | Ringa Tohu Papa | Roadmarkers use machines to apply markings to roads and surfaces such as car parks and sports courts. | Average | N/A | Construction and infrastructure | 40 per hour |
| 131 | Corrections Officer | Δpiha Whare Herehere | Corrections officers are responsible for keeping prisoners safe and secure and motivating them to make changes in their lives. | Good | 1 year | Social and community services | 69K per year, 88K per year |
| 132 | Automotive Technician | Kaihangarau PΕ«kaha Waka | Automotive technicians service and repair vehicles and their parts and systems. | Good | 3-4 years | Manufacturing and technology | 40 per hour, 45 per hour |
| 133 | Automotive Refinisher | Kaipeita Waka | Automotive refinishers prepare vehicle surfaces, match and mix colours, and apply paint to vehicles. | Good | 3-4 years | Manufacturing and technology | 28 per hour, 38 per hour |
| 134 | Carpenter | Kaihanga Whare | Carpenters work mainly with wood to repair or install foundations, walls, roofs, windows and doors in buildings. | Good | 3-4 years | Construction and infrastructure | 25 per hour, 41 per hour |
| 135 | Foreign Policy Officer | Δpiha Take TΔwΔhi | Foreign policy officers represent New Zealand's interests overseas and provide policy advice to the Government on foreign affairs and trade. | Average | 5 years | Social and community services | 75K per year, 140K per year |
| 136 | Crane Operator | Kaiwhakamahi Wakaranga | Crane operators use cranes to move objects such as materials on construction sites, containers on wharves, and heavy parts in factories. | Average | 1-2 years | Construction and infrastructure, Manufacturing and technology | 35 per hour, 70 per hour |
| 137 | Counsellor | Kaitohutohu | Counsellors help people to deal with challenges and manage their emotions, thoughts and behaviour. | Good | 3-5 years | Social and community services | 86K per year, 119K per year |
| 138 | Sales and Marketing Manager | Kaiwhakahaere Hokohoko | Sales and marketing managers plan and direct the development, promotion and sale of an organisation's goods and services. | Good | 3 years | Services industries, Creative industries | 350K per year, 258K per year |
| 139 | Marine Biologist | KaimΔtai Koiora Moana | Marine biologists study animals and plants that live in the sea and freshwater, and how they interact with their surroundings. | Average | 5-9 years | Primary industries | 177K per year, 198K per year |
| 140 | Dentist | Ngaio Niho | Dentists study and treat diseases, injuries and problems of the mouth, teeth, gums and jaw. They also educate patients on how to avoid oral health problems. | Average | 5 years | Social and community services | 254K per year |
| 141 | Psychotherapist | Kaihaumanu Hinengaro | Psychotherapists provide talk therapy to help people manage and improve their mental health and emotional wellbeing. | Average | 3-5 years | Social and community services | 85K per year, 118K per year |
| 142 | Policy Analyst | KaitΔtari Kaupapa | Policy analysts analyse information to assist in the development, interpretation and review of government or industrial policies. | Average | 3 years | Social and community services | 92K per year, 170K per year |
| 143 | Probation Officer | Δpiha Matakana | Probation officers supervise offenders serving a sentence in the community. They also help ex-prisoners return to society. | Good | <1 year | Social and community services | 60K per year, 69K per year |
| 144 | Environmental/βPublic Health Officer | Δpiha Hauora Taiao/βPΔpori | Environmental/public health officers investigate, monitor, assess and advise on food and alcohol safety, disease prevention, disease outbreaks, and environmental hazards such as pollution. | Average | 3 years | Social and community services | 70K per year, 85K per year |
| 145 | Psychologist | KaimΔtai Hinengaro | Psychologists diagnose, treat, and work to prevent a range of psychological problems that affect people's behaviour, thoughts and emotions. | Good | 6-7 years | Social and community services | 70K per year, 150K per year |
| 146 | Clinical Physiologist | KaimΔtai Hinengaro Tiaki TΕ«roro | Clinical physiologists use technical equipment to measure and analyse patients' organs or internal systems, to help doctors diagnose and treat patients. | Good | 3-5 years | Social and community services | 86K per year, 119K per year |
| 147 | Pharmacist | Taka RongoΔ | Pharmacists prepare and dispense prescribed medicine, and discuss conditions and treatments with patients. They may carry out tests and vaccinations. | Good | 5 years | Social and community services, Services industries | 86K per year, 119K per year |
| 148 | Anaesthetic Technician | Kaihangarau Haurehu | Anaesthetic technicians assist anaesthetists during operations, and prepare operating theatres and clinics for anaesthetic procedures. | Good | 3 years | Social and community services | 60K per year, 119K per year |
| 149 | Police Officer | Pirihimana | Police officers work to prevent and solve crime, keep the peace, and respond to criminal activities and emergencies. | Average | <1 year | Social and community services | 83K per year |
| 150 | Security Officer/βGuard | Δpiha Whakamarumaru/βTΕ«tei Whakamarumaru | Security officers/guards protect people, property and assets by investigating, monitoring, controlling and reporting threats. | Good | <1 year | Social and community services, Services industries | 30 per hour, 70 per hour |
| 151 | Fishery Officer | Δpiha Hao Ika | Fishery officers gather information on all aspects of the fishing industry and enforce fisheries laws. | Poor | <1 year | Primary industries, Social and community services | 100K per year |
| 152 | Arborist | Kaitiaki RΔkau | Arborists plant and remove trees, prune branches and treat disease. | Good | 1-3 years | Primary industries | 25 per hour, 35 per hour |
| 153 | Real Estate Agent | MΔngai Hoko Whare/βWhenua | Real estate agents arrange property and house sales for clients. | Average | <1 year | Services industries | |
| 154 | Diagnostic Radiologist | KaimΔtai TΔtari Hihi Irirangi | Diagnostic radiologists diagnose disease and injury using x-rays, ultrasound, MRI, CT, nuclear medicine and radioactive solutions. | Good | 13 years | Social and community services | 205K per year, 251K per year |
| 155 | Gynaecologist/βObstetrician | KaimΔtai Take Wahine/βWhakawhΔnau Tamaiti | Gynaecologists/obstetricians advise, diagnose and treat issues with the female reproductive system, and provide medical care for women before, during and after pregnancy. | Good | 14 years | Social and community services | 205K per year, 251K per year |
| 156 | Anaesthetist | Kairehu | Anaesthetists give anaesthesia (gas or injections to prevent pain) during surgery and other procedures. They assess patients and resuscitate them if necessary. | Good | 13 years | Social and community services | 205K per year, 251K per year |
| 157 | Ranger | Δpiha Papa Atawhai | Rangers protect, enhance and maintain conservation and recreation areas such as regional and national parks, forests, wetlands, reserves, and sites of cultural importance. | Average | N/A | Primary industries, Social and community services | 60K per year, 93K per year |
| 158 | Ophthalmologist | TΔkuta Whakaora Whatu | Ophthalmologists diagnose and treat eye conditions and injuries, and perform eye surgery. | Good | 13 years | Social and community services | 205K per year, 251K per year |
| 159 | Physician | Rata | Physicians are medical specialists who provide non-surgical advice and treatment to patients referred to them by other doctors. | Good | 14 years | Social and community services | 205K per year, 251K per year |
| 160 | Mechanical Engineer | Mataaro PΕ«kaha | Mechanical engineers design and give advice on the building and repair of machines and tools. They also investigate problems and faults with machinery, and study ways to improve manufacturing and energy production. | Good | 3-4 years | Manufacturing and technology | 105K per year, 180K per year |
| 161 | Teacher Aide | KaiΔwhina Kaiako | Teacher aides assist teachers in a classroom by working with students on a one-to-one basis, or in groups. | Average | N/A | Social and community services | 37 per hour |
| 162 | Building Contractor | Kaihanga Whare | Building contractors run their own businesses and plan, supervise and work on the construction and alteration of buildings. | Good | 3-4 years | Construction and infrastructure | |
| 163 | Building and Construction Manager | Kaiwhakahaere Hanga Whare | Building and construction managers plan, control and co-ordinate civil engineering or building projects, and the resources and people involved. | Good | 2-4 years | Construction and infrastructure | 224K per year |
| 164 | Civil Engineering Technician/βDraughtsperson | Kaihangarau/βKaihoahoa Mataaro Metarahi | Civil engineering technicians/draughtspeople plan and draw the technical details for building and repairing roads, bridges, buildings and other structures. | Good | 1-2 years | Manufacturing and technology, Construction and infrastructure | 70K per year, 110K per year |
| 165 | Scaffolder | Kaihanga Rangitupu | Scaffolders design, construct and remove scaffolding around buildings and other structures such as bridges. | Good | 3-5 years | Construction and infrastructure | 37 per hour |
| 166 | Pet Groomer | Kaiwhakapaipai MΕkai | Pet groomers clean, trim and shape the hair and nails of animals in salons, mobile grooming vans and pet shops. | Good | N/A | Services industries | 30 per hour |
| 167 | Veterinary Nurse | Tapuhi Kararehe | Veterinary nurses help assess, treat and care for sick and injured animals. They also interact with clients and perform receptionist duties. | Good | 2-3 years | Services industries, Social and community services, Primary industries | 60K per year |
| 168 | Bartender | Kaitiaki Pae Inu | Bartenders prepare and serve drinks in bars, restaurants and clubs. | Good | N/A | Services industries | 25 per hour, $29 per hour |
| 169 | Cafe Worker | Kaimahi Toa Kawhe | Cafe workers prepare, serve and sell food and drinks to customers at delicatessens, cafes, canteens and takeaway bars. | Good | N/A | Services industries | 30 per hour |
| 170 | Waiter/βWaitress | Kaitiaki TΔpu Kai | Waiters/waitresses serve food and drinks in restaurants, hotels, clubs and other eating places. | Good | N/A | Services industries | $24 per hour |
| 171 | Dairy Herd Manager | Kaiwhakahaere MΔpu Kau | Dairy herd managers run daily dairy farming operations such as feeding and milking cows, monitoring animal health and environmental management. | Good | N/A | Primary industries | 88K per year, 90K per year |
| 172 | Dairy Farm Manager | Kaiwhakahaere PΔmu Kau | Dairy farm managers manage farming operations and staff for dairy farm owners. | Good | N/A | Primary industries | 120K per year, 160K per year |
| 173 | Driller | Kaipoka | Drillers assemble, position, and operate drilling rigs and related equipment to extract ores, liquids, and gases from the earth. | Poor | N/A | Construction and infrastructure | 70K per year, 150K per year |
| 174 | Mining Engineer | Mataaro Waro | Mining engineers plan, prepare, design and manage the development of opencast (above ground) or underground mines. | Average | 4 years | Construction and infrastructure, Manufacturing and technology | 75K per year, 210K per year |
| 175 | Miner/βQuarry Worker | Kaimahi Huke KΕwaro | Miners and quarry workers operate machinery, vehicles and equipment to extract and process minerals and rocks. | Average | N/A | Construction and infrastructure | 80K per year, 150K per year |
| 176 | Mine/βQuarry Manager | Kaiwhakahaere Huke KΕwaro | Mine and quarry managers supervise mine and quarry workers, do safety checks and plan activities in mines and quarries. | Average | N/A | Construction and infrastructure | 155K per year, 210K per year |
| 177 | Farmer/βFarm Manager | Kaiahuwhenua/βKaiwhakahaere PΔmu | Farmers/farm managers manage and work on farms. Farmers own or lease the land, while farm managers operate farms for farm owners. | Good | N/A | Primary industries | $78K per year |
| 178 | Farm Assistant | Kaimahi PΔmu | Farm assistants help farmers with a variety of tasks, including raising and caring for animals, repairs and maintenance, tractor work and other farming activities. | Good | N/A | Primary industries | 60K per year |
| 179 | Financial Adviser | Kaiwhakatakoto Kaupapa PΕ«tea | Financial advisers give advice about financial planning, investing, insurance and other financial services. | Good | 1-2 years | Services industries | 130K per year, 150K per year |
| 180 | Records Adviser | Kaiwhakahaere KΕnae | Records advisers create and monitor electronic and paper filing systems so that records can be filed, found, tracked and disposed of. | Average | N/A | Services industries | 58K per year, $100K or more |
| 181 | Geologist | KaimΔtai Aro Whenua | Geologists study Earth processes, such as earthquakes, floods and volcanic eruptions, to predict future events. They also advise on natural hazards and how to develop or use the Earth's land and resources. | Average | 3-7 years | Services industries, Primary industries | 180K per year |
| 182 | Landscape Architect | Kaihoahoa Whenua | Landscape architects plan, design and advise on the construction of urban, rural, residential and public landscapes. They also manage and conserve natural or heritage landscapes and public open spaces. | Average | 4 years | Construction and infrastructure, Primary industries, Creative industries | 120K per year |
| 183 | Hotel Porter | Kaikawe Tueke | Hotel porters meet and greet guests, answer enquiries, assist with luggage and park guests' vehicles. | Good | N/A | Services industries | 29 per hour |
| 184 | Forensic Scientist | KaipΕ«taiao Taihara | Forensic scientists apply scientific knowledge and skills to investigate crimes and help the police find or eliminate crime suspects. They may research developing or improving forensic techniques. | Poor | 4-5 years | Manufacturing and technology, Social and community services | 88K per year, 166K per year |
| 185 | Urban/βRegional Planner | Kaiwhakamahere TΔone/βRohe | Urban/regional planners develop and administer plans for physical, environmental, social and economic development of urban and rural areas. | Good | 3-4 years | Social and community services | 130K per year |
| 186 | Building Surveyor | KairΕ«ri Whare | Building surveyors inspect plans and building constructions to see if buildings are, or will be, built correctly. They may also issue certificates, write reports and help owners and potential buyers with construction problems and solutions. | Good | N/A | Construction and infrastructure, Social and community services | 112K per year |
| 187 | Interior Designer | KaitΔtai o-Roto | Interior designers plan, design, decorate and furnish spaces in residential, commercial, retail and leisure environments. | Average | 3 years | Construction and infrastructure, Creative industries | 95K per year, 120K per year |
| 188 | Media Producer | KaihautΕ« PΔpΔho | Media producers plan and produce films, television programmes, theatre productions, music, digital content, radio shows, festivals and other artistic activities. | Poor | N/A | Creative industries | |
| 189 | Caretaker | Kaitautiaki | Caretakers keep places such as schools, apartment blocks and public buildings clean, safe and in good order. | Poor | N/A | Services industries, Construction and infrastructure | 26 per hour |
| 190 | Property Manager | Kaiwhakahaere Papa Whenua | Property managers look after the daily running of residential or commercial properties. | Good | N/A | Construction and infrastructure, Services industries | 82K per year, 122K per year |
| 191 | Zoologist | KaipΕ«taiao Kararehe | Zoologists study animals and their behaviour in the wild or in captivity, and how they interact with other species and their environments. | Average | 3 years | Social and community services, Primary industries | 142K per year |
| 192 | Case Manager | Kaiwhakahaere KΔhi | Case managers work with individuals and families to help them overcome hardship, and access social services and support. | Good | <1 year | Social and community services | 80K per year |
| 193 | Model | Kaiwhakakite KΔkahu | Models display and promote clothes or other goods on television, online, in magazines and advertising, and on catwalks at fashion shows. | Poor | N/A | Services industries, Creative industries | |
| 194 | Managing Director/βChief Executive | KaihautΕ« Whakahaere/βTumu Matua | Managing directors/chief executives lead and make overall decisions for an organisation to make sure it operates effectively. | Good | N/A | Services industries | 800K per year, 1.2M per year |
| 195 | Support Worker | KaiΔwhina Tiaki Tangata | Support workers help people with health problems or disabilities to do daily tasks, such as housework, and be as independent as possible. | Good | N/A | Social and community services | 28 per hour |
| 196 | Radiation Therapist | Kaihaumanu PΕ«hihi | Radiation therapists are part of a specialised team that uses radiation to treat cancer and other diseases | Average | 3 years | Social and community services | 98K per year, 124K per year |
| 197 | Entertainer | Kaiwhakangahau | Entertainers perform a variety of acts, such as dance, drama or acrobatics, to entertain an audience. | Poor | N/A | Creative industries | |
| 198 | Film/βTelevision Camera Operator | Kaitango Whakaahua WhitiΔhua/βPouaka Whakaata | Film and television camera operators use digital and film cameras to record events and scenes for television, movies and videos. | Poor | N/A | Creative industries | 50 per hour, 120 per hour |
| 199 | Insurance Adviser | Kaitohutohu Inihua | Insurance advisers give advice about insurance and sell insurance to clients. | Good | N/A | Services industries | 100K per year, 130K per year |
| 200 | Rubbish/βRecycling Collector | Kaiwhakahiato RΔpihi | Rubbish/recycling collectors collect household, industrial or commercial rubbish for disposal or recycling. | Average | N/A | Construction and infrastructure | $23 per hour |
| 201 | Health Services Manager | Kaiwhakahaere Ratonga Hauora | Health services managers are responsible for the day-to-day running of a hospital, primary health organisation (PHO), clinic or community health service. | Good | 3-5 years | Social and community services | 125K per year, 245K per year |
| 202 | Painter and Decorator | Kaipeita/βKaiwhakapaipai Whare | Painters and decorators apply decorative and protective finishes to interior and exterior walls, doors, windows and other surfaces of buildings. | Good | 3 years | Construction and infrastructure | 76K per year |
| 203 | Customs Officer | Δpiha Taupare | Customs officers control the entry and departure of goods, ships, planes and people to and from New Zealand. | Good | <1 year | Social and community services | 73K per year, 125K per year |
| 204 | Marketing Specialist | Ngaio Whakatairanga | Marketing specialists develop and implement plans for promoting an organisation's goods, services and ideas. | Average | 3 years | Services industries | 90K per year, 170K per year |
| 205 | Dietitian | PΕ«kenga Whakaita Kai (NgΔ Tohunga MΔtai Kai) | Dietitians provide advice and counselling about diet, food and nutrition to individuals and communities. They also design nutrition programmes to support health and wellbeing. | Average | 5 years | Services industries, Social and community services | 86K per year, 119K per year |
| 206 | Agricultural/βHorticultural Consultant | Kaitohutohu Ahuwhenua | Agricultural/horticultural consultants advise farmers, growers and organisations on business, production and land management solutions. | Good | 3 years | Primary industries | 85K per year, 150K per year |
| 207 | Management Consultant | Kaitohutohu Whakahaere | Management consultants work with organisations to solve problems and recommend improvements to strengthen business performance. | Average | 3 years | Services industries | 71K per year, 102K per year |
| 208 | Earthmoving Machine Operator | Kaiwhakamahi Wakapana Oneone | Earthmoving machine operators use digging machines, such as bulldozers or graders, to move, shape or level earth, rock and rubble. | Good | <1 year | Construction and infrastructure | 40 per hour |
| 209 | Bank Worker | Kaimahi Whare PΕ«tea | Bank workers receive deposits and pay out money, keep records of transactions, issue receipts, and advise customers on banking services and products. | Average | N/A | Services industries | 80K per year, 145K per year |
| 210 | Actor | Kaitapere | Actors entertain people by using body movement and speech to play a character in media and stage productions. | Poor | N/A | Creative industries | |
| 211 | Purchasing/βSupply Officer | Δpiha Hoko | Purchasing/supply officers buy or supply equipment, materials and services at the best price and quality for an organisation. | Good | N/A | Manufacturing and technology, Services industries | 100K per year, 170K per year |
| 212 | Chemical Production Operator | Kaiwhakamahi Whakaputa MatΕ« | Chemical production operators perform a variety of tasks involved in producing toiletries or pharmaceutical products, including ointments, creams, aerosols, tablets, capsules, bandages and vaccines. | Poor | N/A | Manufacturing and technology | 28 per hour |
| 213 | Food and Beverage Factory Worker | Kaimahi TohitΕ« Whakanao Kai/βInu | Food and beverage factory workers prepare ingredients, operate machinery, and bottle or package food and drink. | Average | N/A | Manufacturing and technology | 25 per hour |
| 214 | Packhouse Worker | Kaimahi Whare Putunga | Packhouse workers grade, pack and store fruit, vegetables and other produce in packhouses. | Good | N/A | Primary industries | 25 per hour |
| 215 | Production Manager | Kaiwhakahaere Whakaputa | Production managers organise and control the production process in a factory. They ensure that products are made to the right specifications and are ready on time and within budget. | Average | 1-3 years | Manufacturing and technology | 150K per year |
| 216 | Food Technologist | Kaihangarau Kai | Food technologists research, develop and improve food and drink products and their processing, packaging, storage and safety. | Good | 3-5 years | Manufacturing and technology, Primary industries | 75K per year, 140K per year |
| 217 | Finance Manager | Kaiwhakahaere PΕ«tea | Finance managers oversee the major financial operations of an organisation. | Good | 3 years | Services industries | 117K per year, 170K per year |
| 218 | Health and Safety Adviser | Kaitohutohu Hauora-Haumaru | Health and safety advisers monitor workplace health and safety hazards, train employees on health and safety procedures, and investigate accidents. | Good | 1-3 years | Services industries | 70K per year, 125K per year |
| 219 | Dancer | Kaikanikani | Dancers entertain people by expressing ideas and emotions, usually to music, using body movements. | Poor | N/A | Creative industries | |
| 220 | Surveyor | KairΕ«ri | Surveyors plan, direct and conduct survey work to determine the position of boundaries, locations, topographic features and built structures. | Good | 4 years | Construction and infrastructure | 110K per year |
| 221 | Intelligence Officer | Δpiha Matataua | Intelligence officers collect and analyse information on people, places and events that may be a threat to businesses or national and international security. | Good | 1-3 years | Services industries, Social and community services | 86K per year, 170K per year |
| 222 | Welder | Kaihonohono Maitai | Welders make, join and repair metal parts for machinery and equipment using welding techniques. | Good | 1-3 years | Manufacturing and technology, Construction and infrastructure | 65 per year, 125K per year |
| 223 | Accountant | Kaikaute PΕ«tea | Accountants provide accounting services to companies, organisations and individuals. They prepare financial statements and forms, and advise clients on financial aspects of business. | Good | 2-6 years | Services industries | 165K per year |
| 224 | Physiotherapist | Kairomiromi | Physiotherapists help people regain movement and function after they have been affected by an injury, disability or health condition. They also give advice on how to prevent injuries. | Good | 4 years | Social and community services | 86K per year, 119K per year |
| 225 | Community KaritΔne | KaritΔne β Hapori WhΔnui | Community karitΔne offer support to families with children under the age of five, and provide information on parenting issues such as breastfeeding, infant nutrition, sleeping and child behaviour. | Poor | 1 year | Social and community services | 83K per year |
| 226 | Health Care Assistant | KaiΔwhina Haumanu Hauora/βKaimahi Atawhai | Health care assistants care for people in aged residential care, private homes, hospitals and disability support. | Good | 1-2 years | Social and community services | 34 per hour |
| 227 | Registered Nurse | Tapuhi Whai RΔhitatanga | Registered nurses assess, treat and support people who are sick, disabled or injured, in hospitals, clinics, rest homes, and nursing homes. | Good | 3 years | Social and community services | 84K per year, 153K per year |
| 228 | Solicitor | RΕia | Solicitors give legal advice, prepare legal documents and study the details of legal arguments. | Poor | 3-4 years | Services industries | 131K per year |
| 229 | Barrister | RΕia KΕti | Barristers give legal advice and appear on behalf of clients in civil, family and criminal cases in courts and tribunals. | Average | 3-4 years | Services industries | 131K per year |
| 230 | Plumber, Gasfitter and Drainlayer | Kaiwhakarerewai, KaiwhakarerekorohΕ«, Kaiwhakatakoto Paipa Wai | Plumbers, gasfitters and drainlayers assemble, install and repair pipes, drains and fixtures and fittings that supply water and gas or remove waste. | Good | 2-4 years | Construction and infrastructure | 53 per hour |
| 231 | Roading Construction Worker | Kaimahi Hanga Rori | Roading construction workers make surfaces such as roads, airport runways and driveways, and control traffic around road construction sites. | Good | N/A | Construction and infrastructure | 24 per hour, $85,000 a year. |
| 232 | Building and Construction Labourer | Kaimahi Whaihanga | Building and construction labourers do a wide range of physical work on building sites, roads and other large civil construction projects. | Good | N/A | Construction and infrastructure, Primary industries | 33 per hour, 40 per hour |
| 233 | Biomedical Engineer | Mataaro RongoΔ Koiora | Biomedical engineers design, build and maintain medical equipment, artificial body parts and computer programs to help treat disabilities, diseases, or injuries. | Good | 4 years | Social and community services, Manufacturing and technology | 55K per year, 130K per year |
| 234 | Health Promoter | Kaiwhakatairanga Hauora | Health promoters work with communities and groups to develop ways to improve peopleβs health. They also work with government agencies to improve environmental conditions. | Average | 1-3 years | Social and community services | 86K per year, 119K per year |
| 235 | Radiation Oncologist | KaimΔtai Mate Pukupuku | Radiation oncologists provide radiation treatment and management of patients with cancer and other medical conditions. | Good | 13 years | Social and community services | 205K per year, 251K per year |
| 236 | Economist | Ngaio Εhanga | Economists analyse financial, labour and trade markets, and predict or explain economic events. | Poor | 3-5 years | Services industries | 125K per year |
| 237 | Court Registry Officer | Δpiha Whakarite KΕti/βTure | Court registry officers assist with the day-to-day operation of courts. They handle court documents, schedules and may support the judge in running court hearings. | Average | N/A | Social and community services | 81K per year |
| 238 | Beauty Therapist | Kaihaumanu Kanohi | Beauty therapists provide treatments such as facials, massage, laser hair removal, waxing and pedicures. | Average | 1-2 years | Services industries, Creative industries | 70K per year |
| 239 | Sales Representative | Kanohi Hokohoko | Sales representatives promote, market and sell products or services to business and professional establishments, or wholesale or retail outlets. | Average | N/A | Services industries | 85K per year, $100K or more per year |
| 240 | Oral Health Therapist | Kaiakuaku Waha | Oral health therapists provide dental care, treat gum disease and teach people how to care for teeth and gums. They may refer clients to dentists. | Good | 3 years | Social and community services | 83K per year, 119K per year |
| 241 | Film and Video Editor | Δtita Kiriata/βΔtita Ataata | Film and video editors assemble video, graphics, audio and text into a finished product for films, television programs, video productions or commercials. | Poor | N/A | Creative industries | $27 per hour |
| 242 | Production Assistant (Film, Television, Radio or Stage) | KaiΔwhina Whakarite (Kiriata, Pouaka Whakaata, Irirangi, Whakaari rΔnei) | Production assistants help production teams organise the making of film, television, radio or stage productions. | Poor | N/A | Services industries, Creative industries | |
| 243 | Musician | Kaiwhakatangitangi | Musicians write, arrange, conduct, and perform musical compositions. | Poor | N/A | Creative industries | |
| 244 | Industrial Designer | KaitΔtai Ahumahi | Industrial designers design and develop innovative products for use in homes, businesses and industry. | Average | 3 years | Manufacturing and technology, Services industries, Creative industries | 75K per year, 120K per year |
| 245 | Psychiatrist | Rata Mate Hinengaro | Psychiatrists diagnose and treat mental illness and emotional and behavioural disorders by providing psychotherapeutic treatment and psychiatric medication. | Good | 12 years | Social and community services | 205K per year, 251K per year |
| 246 | Funeral Director/βEmbalmer | KaihautΕ«/βKaiwhakapaipai TΕ«pΔpaku | Funeral directors/embalmers organise and direct funerals, register deaths, and prepare human bodies for visits by families, and for burial or cremation. | Poor | 1-2 years | Services industries | 60K per year |
| 247 | Speech-Language Therapist | Kaihaumanu Reo Δ-Waha | Speech-language therapists assess and treat people who have problems with verbal communication or swallowing. This may include difficulties with speech, language, listening, reading or writing. | Good | 4-5 years | Social and community services | 119K per year |
| 248 | Firefighter | Kaitinei Ahi | Firefighters control and put out fires, help rescue people and animals, and educate the public about fire safety and fire prevention. | Poor | <1 year | Social and community services | 77K per year, 108K per year |
| 249 | Medical Physicist | KaiahupΕ«ngao Whakaora | Medical physicists help plan radiation treatment for patients, check and monitor radiation equipment, and develop new treatment techniques. | Good | 8 years | Services industries | 118K per year, 161K per year |
| 250 | Quarantine Officer | Δpiha Δrai Mate HΕrapa | Quarantine officers identify and control biosecurity risks at New Zealand's borders by inspecting goods and vessels arriving in the country. | Average | N/A | Social and community services, Primary industries | 71K per year, 80K per year |
| 251 | Lift Technician | Kaihangarau Waka Kawe | Lift technicians install, maintain and repair lift and escalator systems. | Average | 3-4 years | Manufacturing and technology, Construction and infrastructure | 75K per year, 100K per year |
| 252 | Table Games Dealer | Kaimahi Wharepeti | Table games dealers lead and control games played at casinos, calculate winnings and losses, and pay out winning bets. | Average | <1 year | Services industries | |
| 253 | Massage Therapist | Kaihaumanu Mirimiri | Massage therapists manipulate the soft tissue of people's bodies to treat health problems and to help people relax. | Average | N/A | Services industries | |
| 254 | Landscaper | Kaihoahoa Whakapaipai Whenua | Landscapers design, develop, maintain and remodel gardens and landscapes. | Average | N/A | Construction and infrastructure, Primary industries | 25-$36 per hour |
| 255 | Stonemason | Pouwhakanao KΕhatu | Stonemasons work with stone to construct or renovate buildings, fittings, walls and paving, or to create monuments. | Good | 2-3 years | Construction and infrastructure, Creative industries | 40 per hour |
| 256 | Actuary | Kaitauwhiro PΕ«tea | Actuaries predict and assess the financial risks and impacts of future events. They work in areas such as insurance, superannuation and investment. | Good | 5-8 years | Services industries | 165K per year, 180K per year |
| 257 | Marine Engineer | Mataaro Kaipuke | Marine engineers operate, service and repair engines, and mechanical and electronic equipment on ships and boats. | Poor | 1-3 years | Manufacturing and technology | 80K per year, 184K per year |
| 258 | Fabrication Engineer | Mataaro Piharoa | Fabrication engineers make, install and repair metal products such as vents, handrails, boilers, aircraft and boat parts, or beams and girders for construction projects. | Average | 1-3 years | Construction and infrastructure, Manufacturing and technology | 70K per year, $80K or more per year |
| 259 | Sterilising Technician | Kaihangarau Whakahoromata | Sterilising technicians clean, sterilise and package surgical instruments and other hospital equipment, soft goods and linen in a sterilisation unit. | Good | 2 years | Social and community services | 62K per year, 75K per year |
| 260 | Art Director (Film, Television or Stage) | Kaitohu Toi (Kiriata, Pouaka Whakaata, Whakaari rΔnei) | Art directors plan, organise and control artistic aspects of film, television or stage productions. | Poor | N/A | Creative industries | |
| 261 | Clinical Coder | Kaiwhakararangi Tohu Hauora | Clinical coders convert information in patient discharge notes into health classification codes. This information is used for research and to plan health funding and services. | Good | <1 year | Social and community services | 88K per year, $93K per year |
| 262 | Joiner | Kaihanga Taonga Δ-Whare | Joiners use timber and board products to make fittings such as cabinets, doors, window frames and stairs. | Good | 3-4 years | Construction and infrastructure, Manufacturing and technology | 25 per hour, 35 per hour |
| 263 | Collision Repair Technician | Kaihangarau Tinana Waka | Collision repair technicians repair and replace damaged body parts of cars and other vehicles. | Good | 3-4 years | Manufacturing and technology, Services industries | 75 per year |
| 264 | Diversional and Recreational Therapist | Kaihaumanu RΔhia | Diversional and recreational therapists design and run recreation and leisure programmes to support and enhance people's total wellbeing. | Good | N/A | Services industries, Social and community services | 35 per hour, 50 per hour |
| 265 | Occupational Therapist | Kaiwhakaora Ngangahau | Occupational therapists provide therapy and support to people with limited ability to carry out everyday activities because of illness, injury or disability. | Average | 3 years | Social and community services | 119K per year |
| 266 | Elected Government Representative | MΔngai Δ-PΕtitanga | Elected government representatives are elected by the people of a specific area to help govern a city, region or country. | Poor | N/A | Social and community services | 300K per year, 190K per year |
| 267 | Window Cleaner | Kaihoroi Matapihi | Window cleaners clean windows and other glass in shops, schools, offices, hospitals and homes. | Good | N/A | Services industries | 23-$35 per hour |
| 268 | Health and Safety Inspector | KaimΔtai Hauora-Haumaru | Health and safety inspectors assess workplaces and work activities to determine if employers are keeping workers and other people safe and healthy at work. They also educate people about health and safety, investigate accidents and lead prosecutions. | Poor | 1-2 years | Services industries, Social and community services | 66K per year, 93K per year |
| 269 | Career Consultant | Kaitohutohu Umanga | Career consultants help clients with career decision-making and development, job hunting, and returning to work after illness or accident. | Poor | 2-5 years | Social and community services | 103K per year |
| 270 | Surgeon | Rata HΔparapara | Surgeons consult with patients and operate on people to treat and manage disease and injuries. | Good | 13-15 years | Social and community services | 205K per year, 251K per year |
| 271 | Pathologist | KaimΔtai Mate Tangata | Pathologists are doctors who diagnose and study human diseases and conditions. They diagnose health problems by testing tissue and fluid samples taken from patients. | Good | 13-15 years | Social and community services | 205K per year, 251K per year |
| 272 | Business Analyst | KaitΔtari Pakihi | Business analysts design or recommend solutions, such as computers or computer programs, to help organisations meet their goals. | Good | 2-3 years | Manufacturing and technology | 140K per year, 160K per year |
| 273 | Information Technology Helpdesk/βSupport Technician | Kaihangarau Δwhina Hangarau PΔrongo | Information technology (IT) helpdesk/support technicians set up computer and IT equipment and identify and fix hardware and software problems | Good | 1-3 years | Manufacturing and technology | 90K per year, 120K per year |
| 274 | Immigration Officer | Δpiha Whakahaere Manene | Immigration officers control the entry of people from other countries into New Zealand, assessing visa applications from people who wish to visit, study, work or reside in NZ. | Good | <1 year | Social and community services | 91K or more per year |
| 275 | Security Analyst | KaitΔtari Whakamarumaru | Security analysts create and monitor security processes and frameworks to protect an organisations information systems and computer networks from being illegally accessed. | Good | 1-4 years | Manufacturing and technology | 200K per year, 500K per year |
| 276 | Security Consultant | Kaitohutohu Whakamarumaru | Security consultants identify security weakness in information technology (IT), advise on IT security, and design IT security systems. | Good | 2-3 years | Manufacturing and technology | 200K per year, 500K per year |
| 277 | Test Analyst | KaitΔtari WhakamΔtautau | Test analysts design and carry out tests for computer software and systems, analyse results, and identify and report problems. | Good | 1-3 years | Manufacturing and technology | 130K per year, 200K per year |
| 278 | Scrum Master | Kaitakawaenga Kakari | Scrum masters use the scrum software development approach to keep IT project teams on track. They also help remove obstacles to progress. | Good | 2-3 years | Manufacturing and technology | 200K per year |
| 279 | Penetration Tester | KaiwhakamΔtautau MΕ«rere | Penetration testers investigate security weaknesses in online systems and databases. | Good | 1-4 years | Manufacturing and technology | 200K per year |
| 280 | Emergency Management Officer | Δpiha Whakahaere Ohotata | Emergency management officers plan for and respond to emergencies such as earthquakes and weather events. They also train communities to prepare for disasters. | Average | >1 year | Social and community services | 79K per year, 95K per year |
| 281 | Pharmacy Technician | Kaihangarau RongoΔ | Pharmacy technicians help pharmacists to prepare and give out medicines. | Good | 2 years | Services industries, Social and community services | 80K per year |
| 282 | Architect | Kaihoahoa Whare | Architects plan, design and advise on the construction and alteration of buildings and other structures. | Average | 8-10 years | Construction and infrastructure, Manufacturing and technology, Creative industries | 90K per year, 140K per year |
| 283 | Architectural Technician | Kaihangarau Hoahoa Whare | Architectural technicians create drawings and make models of building structures, research construction materials, and assist with building consent processes. | Average | 2 years | Construction and infrastructure, Manufacturing and technology, Creative industries | 125K per year |
| 284 | Receptionist | Kaiwhakatau Manuhiri | Receptionists greet visitors and clients, and deal with enquiries and requests. Their work includes administration tasks such as answering the telephone, scheduling appointments and keeping records. | Average | N/A | Services industries | 65K per year |
| 285 | Data Entry Operator/βTranscriptionist | Kaiwhakauru Raraunga/βKaipatopato KΕrero | Data entry operators/transcriptionists copy or transcribe information that is spoken or written. | Average | N/A | Services industries | 55K per year |
| 286 | Game Developer | Kaihanga TΔkaro | Game developers write, design, program, animate and test games for computers, gaming consoles, tablets and mobile phones. | Poor | 2-3 years | Manufacturing and technology, Creative industries | 80K per year, 100K per year |
| 287 | Software Developer | Kaihanga PΕ«manawa Rorohiko | Software developers create and maintain computer software, websites and software applications (apps). | Good | 1-3 years | Manufacturing and technology | 160K per year |
| 288 | Electrical Engineering Technician | Kaihangarau Take PΕ«hiko | Electrical engineering technicians interpret the designs and technical instructions of electrical engineers, then ensure they are accurately carried out. They also develop, install, test and maintain equipment that produces, transmits or uses power. | Good | 2-3 years | Manufacturing and technology | 27 per hour, 43 per hour |
| 289 | Electrician | Kaimahi Hiko | Electricians test, install, maintain and repair electrical systems and equipment. | Good | 3-4 years | Construction and infrastructure, Manufacturing and technology | 43 per hour |
| 290 | Refrigeration/βAir-conditioning Technician | Kaihangarau PΕ«rere WhakamΔtao | Refrigeration/air-conditioning technicians install, service and maintain refrigeration and air-conditioning systems. | Good | 0-4 years | Manufacturing and technology | 95K per year |
| 291 | Social Worker | Kaimahi Toko i te Ora | Social workers provide care, advice and support to people with personal or social problems, and help with community and social issues. | Average | 4-6 years | Social and community services | 75K per year, 118K per year |
| 292 | Biomedical Technician | Kaihangarau Utauta Whakaora | Biomedical technicians make, modify, maintain and repair mechanical and electronic medical equipment such as clinical machines, surgical instruments and implants. | Good | 2 years | Manufacturing and technology, Social and community services | 86K per year, 104K per year |
| 293 | Tour Guide | KaiΔrahi RΕpΕ« Haere | Tour guides escort people on sightseeing, educational or other tours, and describe points of interest. | Poor | N/A | Services industries | |
| 294 | Advertising Specialist | Ngaio PΔnui Toko | Advertising specialists create, co-ordinate, plan and implement advertising campaigns to sell products or services for clients. | Average | N/A | Services industries, Creative industries | 150K per year |
| 295 | Copywriter | Kaituhi PΔnui | Copywriters design and create print, digital, social media, video, television and radio advertisements. | Poor | N/A | Creative industries | 60K per year, 160K per year |
| 296 | Graphic Pre-press Worker | Kaimahi Whakairoiro (Mua Perehi) | Graphic pre-press workers use computers to prepare text and designs for printing. | Poor | 3 years | Manufacturing and technology, Creative industries | 50K per year, 80K per year |
| 297 | Animator/βDigital Artist | Kaiwhakahauora/βRinga Toi Mamati | Animators and digital artists use software, models, photography and drawings to create still and moving images for advertisements, film, print, web or television. | Average | 3 years | Creative industries | 100K per year, 120K per year |
| 298 | Artist | Kaitoi | Artists turn creative ideas into works of art using media such as paint, digital resources, fabric and feathers, clay, stone and wood. | Poor | N/A | Creative industries | |
| 299 | Graphic Designer | Kaihoahoa Whakairoiro | Graphic designers create artwork or designs for printed and electronic media such as magazines, television and websites. | Average | 3 years | Services industries, Creative industries | 105K per year |
| 300 | Aeroplane Pilot | Kaiwhakahaere Waka Rererangi | Aeroplane pilots fly planes that transport people and goods, or spread fertiliser or bait. | Good | 2-3 years | Services industries | , 148K per year |
| 301 | Flight Instructor | Kaiwhakaako Waka Rererangi | Flight instructors teach people how to fly aeroplanes, helicopters or other aircraft. | Good | 2-3 years | Services industries | 70K per year, 100K per year |
| 302 | Aircraft Maintenance Engineer | Mataaro Whakatika Waka Rererangi | Aircraft maintenance engineers keep aeroplanes safe. They install, inspect, maintain and repair aircraft, aircraft radio, avionic (electronic), navigation, communication and electrical and mechanical systems. | Good | 3-5 years | Manufacturing and technology | 91K per year, 165K per year |
| 303 | Army Officer | Δpiha Ope TauΔ | Army officers train army soldiers, manage field exercises and lead soldiers in combat, security operations, peacekeeping missions and disaster relief. | Average | 1-2 years | Services industries, Social and community services | 74K per year, 133K per year |
| 304 | Army Soldier | HΕia Ope TauΔ | Army soldiers defend their country, forming highly skilled teams that work together in combat and security operations, peacekeeping missions, humanitarian assistance and disaster relief. | Good | <1 year | Services industries, Social and community services | 101K-$120K per year |
| 305 | Beekeeper | Kaitiaki PΔ« | Beekeepers look after beehives in apiaries that produce honey, wax, pollen and other bee products. They may also offer pollination services to horticultural and seed crop producers. | Average | 1-2 years | Primary industries | 70K per year, 120K per year |
| 306 | Audiologist/βAudiometrist | KaimΔtai Ororongo/βKaimΔtau Ororongo | Audiologists and audiometrists study, identify, measure and treat hearing loss and ear disorders. They also provide aids and other listening devices to assist patients with hearing loss. | Good | 2-5 years | Social and community services | 120K per year, 75K per year |
| 307 | Agricultural Technician | Kaihangarau Ahuwhenua | Agricultural technicians perform tests and experiments, and provide technical support to assist agricultural scientists in areas such as research, production, servicing and marketing. | Average | 3 years | Primary industries, Services industries | 65K per year, 85K per year |
| 308 | Archivist | Kaitiaki PΕ«ranga | Archivists assess, organise, store and provide access to records and documents of long-term historical or research value. They also advise people and organisations about their archives. | Average | 3-5 years | Services industries, Creative industries | 60K per year, 70K per year |
| 309 | Chef | PΕ«kenga Tao Kai | Chefs prepare and cook food in restaurants, hotels, catering businesses, rest homes, cafes and bars. | Good | 1-3 years | Services industries | 25 per hour, 38 per hour |
| 310 | Cook | Ringawera | Cooks prepare, cook and serve food. They work in cafes, bars, hospitals, schools, daycare centres, fast food outlets, or for caterers. | Good | N/A | Services industries | 27 per hour |
| 311 | Kitchenhand | Ringawera | Kitchenhands wash dishes and clean the kitchen and serving areas in eating places. They may also help kitchen staff prepare food. | Good | N/A | Services industries | $23 per hour |
| 312 | Biochemist | Kairarau MatΕ« Koiora | Biochemists study the chemical structure and function of animals, plants and micro-organisms such as bacteria and viruses. They use this research to develop medical, industrial and agricultural products. | Average | 3-9 years | Manufacturing and technology, Primary industries | 75K per year, 130K per year |
| 313 | Biotechnologist | Ringa Hangarau Koiora | Biotechnologists use their knowledge of living things to develop new animal or plant products such as medicines and pest-resistant crops. | Good | 3-5 years | Manufacturing and technology, Primary industries | 75K per year, 130K per year |
| 314 | Courier/βDelivery Agent | Kaikawe Karere/βUtanga | Couriers/delivery agents sort, collect and deliver mail, packages, parcels and other goods to homes and businesses. | Average | <1 year | Services industries | 130K per year |
| 315 | Delivery Driver | Kaitaraiwa Waka Whakarato | Delivery drivers distribute and may sell products to commercial and home delivery customers. | Good | N/A | Services industries | 25 per hour |
| 316 | Buyer | Kaihoko | Buyers purchase goods to sell in warehouses, shops or department stores. | Average | N/A | Services industries | 60K per year, $60K-140K per year |
| 317 | Chiropractor | Kaikorohiti | Chiropractors treat disorders related to the spine and nervous system to relieve pain and improve the function of nerves, muscles and joints. | Good | 5 years | Social and community services | 60K per year, 150K per year |
| 318 | Brewer | KaitoroΔ« | Brewers use brewing equipment and processes to convert malted barley or other grains into beer, and control or manage the production and packaging of beer. | Average | N/A | Services industries, Manufacturing and technology | 60K per year, 120K per year |
| 319 | Customs Broker/βFreight Forwarder | Kaiwhakawhiti Taupare/βUtanga | Customs brokers and freight forwarders arrange the clearance (through customs) and collection of imported cargo, and the shipment of cargo for export. | Average | <1 year | Services industries | 90K per year, 70K per year |
| 320 | Cabinet Maker | Kaihanga Kapata Taonga | Cabinet makers make and repair fittings and furniture for homes, businesses and boats. | Good | 3-4 years | Manufacturing and technology, Construction and infrastructure | 60K per year |
| 321 | Chemist | Kairarau MatΕ« | Chemists study the make-up and behaviour of chemicals, and may use their findings to develop new products and processes. | Good | 5 years | Manufacturing and technology, Services industries | 120K per year, $150K per year |
| 322 | Dairy Farmer | Kaiahuwhenua Miraka Kau | Dairy farmers plan and manage milk production by cows, maintain pasture and monitor environmental impacts on farms. | Good | N/A | Primary industries | 67K per year, 103K per year |
| 323 | Dairy Farm Assistant | KaiΔwhina PΔmu Kau | Dairy farm assistants help farmers with a variety of tasks, including caring for and milking cows, repairs and maintenance, and other farming activities. | Good | N/A | Primary industries | 80K per year |
| 324 | Sharemilker | Kaiwhakahaere MΔ«raka Kau | Sharemilkers either milk a dairy farmer's cows for a profit share, or own a herd of cows and milk them on an owner's land for a profit share. | Good | N/A | Primary industries | 97K per year |
| 325 | Dental Assistant | KaiΔwhina Mahi Niho | Dental assistants help dentists with patient care and running dental practices. | Good | N/A | Social and community services | 51K-$62K per year |
| 326 | Dental Technician | Kaihangarau Niho | Dental technicians create and repair devices for the treatment, replacement and protection of damaged, badly positioned or missing teeth. | Good | 3 years | Social and community services | 60K-$75K per year |
| 327 | Diver | Kairukuruku | Commercial divers develop and maintain underwater structures and do research for scientists. Recreational divers teach scuba diving and may carry out marine searches and rescues. | Average | <1 year | Construction and infrastructure, Services industries | 49K per year, 1200 per day |
| 328 | Driving Instructor | Kaiwhakaako Taraiwa Waka | Driving instructors teach people how to drive, and instruct experienced drivers how to advance their driving skills and road safety knowledge. | Average | <1 year | Services industries | 80K per year |
| 329 | Deckhand | Ringa Paparahi | Deckhands may take care of passengers and assist in the operation of vessels such as harbour ferries and charter boats, or cast and haul in nets, lines or pots, and process fish on inshore or deep-sea fishing vessels. | Good | N/A | Services industries | 55K per year, 90K per year |
| 330 | Editor | Δtita/βKaiwhakatika | Editors plan, commission, evaluate, select, edit and organise material for publication online or in books, magazines, and newspapers. They may also manage editorial staff. | Average | 1-3 years | Creative industries | 60K per year, 90K per year |
| 331 | Dispensing Optician | Ngaio MΕhiti | Dispensing opticians interpret prescriptions from optometrists and ophthalmologists (eye specialists) for glasses or contact lenses, assemble and fit glasses, and sell customers frames and lenses. | Good | 1-2 years | Social and community services | 60K per year, 85K per year |
| 332 | Debt Collector | Kaikohi Nama | Debt collectors help businesses and individuals collect money or goods from people with overdue debts. | Average | N/A | Services industries | 65K per year |
| 333 | Office Administrator | Kaiwhakarite Δ-Tari | Office Administrators help ensure organisations run efficiently. | Average | N/A | Services industries | 65K per year |
| 334 | Office Manager | Kaiwhakahaere Tari | Office managers are responsible for a range of tasks that make an office run smoothly, including administrative duties, staff supervision and financial work. | Average | N/A | Services industries | 85K per year |
| 335 | Event Manager | Kaiwhakahaere TauwhΔinga | Event managers plan, promote and run events, such as conferences, for a variety of clients. | Good | N/A | Services industries, Creative industries | 50K per year, 75K per year |
| 336 | Maitre dβHotel | Kaiwhakarite HΕtera | Maitres d'hotel oversee the service of food and beverages to guests in restaurants and other eating places. They also check reservations, greet guests and supervise the waiting staff. | Good | N/A | Services industries | $27 per hour |
| 337 | Aquaculture Farmer | Kaiahumoana | Aquaculture farmers manage the breeding, raising and harvesting of fish and shellfish for commercial purposes in marine or freshwater farms. | Good | N/A | Primary industries | 65K per year, 88K per year |
| 338 | Fishing Skipper | Kaiurungi Hao Ika | Fishing skippers are responsible for running a fishing boat. Responsibilities range from navigating the vessel and organising the crew to catching and processing fish. | Average | <2 years | Primary industries | 80K per year, 280K per year |
| 339 | Flight Attendant | TΕ«mau Waka Rererangi | Flight attendants make sure that passengers travelling in aeroplanes are safe and comfortable. | Poor | <1 year | Services industries | 50K per year |
| 340 | Fashion Designer | Kaihoahoa KΔkahu | Fashion designers design clothing and accessories. | Poor | 1-3 years | Manufacturing and technology, Creative industries | 50K per year, 160K per year |
| 341 | Technical Writer | Kaitito Hangarau | Technical writers create content for printed and online media, such as user guides and webpages, and present it in a way that can be easily accessed and understood. | Good | N/A | Manufacturing and technology, Services industries | 60K per year, 130K per year |
| 342 | Engineering Machinist | Kaiwhakamahi PΕ«rere | Engineering machinists create, assemble and repair metal products by interpreting designs, measuring metals, and operating machines to cut and shape them. | Good | 4 years | Manufacturing and technology | 48K-$103K per year |
| 343 | Exhibition and Collections Technician | Kaihangarau Whakaaturanga/βKohinga | Exhibition and collections technicians prepare, install, maintain and dismantle museum and art gallery exhibitions. | Poor | 1-3 years | Services industries, Creative industries | 55K per year |
| 344 | Forestry and Logging Worker | Kaimahi Waonui/βTope RΔkau | Forestry and logging workers plant, maintain, measure, cut and clear trees from forests. | Good | >1 year | Primary industries | 65K per year, 75K per year |
| 345 | Electronics Trades Worker | Ringarehe TΔhiko | Electronics trades workers assemble, install and fix electronic parts and equipment. | Good | 3-4 years | Manufacturing and technology | 80K per year, 120K per year |
| 346 | Furniture Finisher | Kaiwhakaoti Taonga RΔkau | Furniture finishers prepare the final surface of items of furniture and apply stain, lacquer, paint, oil or wax. | Average | 3-4 years | Manufacturing and technology, Creative industries | 60K per year |
| 347 | Cutter | Ringa Tapahi | Cutters lay out and cut fabric to make clothing and soft furnishings. | Average | >1 year | Manufacturing and technology, Creative industries | 26 per hour |
| 348 | Garment Technician | Kaihangarau PΕ«eru | Garment technicians choose clothing fabrics and designs, and make sure clothes are made to quality standards. | Average | 1 year | Manufacturing and technology, Creative industries | 48K-$70K per year |
| 349 | Sewing Machinist | Kaiwhakamahi PΕ«rere Tuitui | Sewing machinists stitch together clothing, canvas for tents and awnings, and soft furnishings. | Good | <1 year | Manufacturing and technology, Creative industries | 25 per hour |
| 350 | Hairdresser/βBarber | Kaikutikuti Makawe | Hairdressers/barbers cut, colour and style hair. Barbers also shave and trim hair, moustaches and beards. | Average | 2-4 years | Services industries, Creative industries | 55K per year, 100K per year |
| 351 | Geospatial Specialist | Ngaio Papa Whenua | Geospatial specialists gather and analyse geographic and spatial (location-based) information and use specialist software to present it in user-friendly formats such as maps and 3D models. | Good | 3 years | Services industries, Construction and infrastructure | 65K per year, 120K per year |
| 352 | Historian | Pouherenga KΕrero o-Mua | Historians research, write and present information about events and people of the past and present. They may also teach history. | Poor | >5 years | Services industries, Creative industries | 120K per year |
| 353 | Insurance Claims Officer | Δpiha Tono RΔ«anga | Insurance claims officers decide whether an insurance company will pay out a claim. | Average | N/A | Services industries | 75K per year, 140K per year |
| 354 | Jeweller | Kaihanga Taonga Rei | Jewellers design, make, alter and repair items such as rings, bracelets, necklaces and earrings. | Average | 4 years | Manufacturing and technology, Services industries, Creative industries | 60K per year, 100K per year |
| 355 | Gardener | Kaitiaki MΔra | Gardeners plant and maintain lawns, trees, shrubs and flowers in public or private gardens and parks. | Average | N/A | Primary industries, Services industries | 60 per hour |
| 356 | Holiday Park Manager | Kaiwhakahaere Papa RΔhia | Holiday park managers run holiday parks, camping grounds, motor camps, caravan parks or seaside resorts as owners or on behalf of owners. | Good | N/A | Services industries | 60K per year |
| 357 | Industrial Spray Painter | Kairehu Ahumahi | Industrial spray painters prepare, paint, powder-coat or resurface industrial parts and machinery and other items used in homes, offices and industries. | Average | N/A | Manufacturing and technology | 62K per year |
| 358 | Author | Kaituhi Pukapuka | Authors write stories, scripts, poems, blogs or plays for publication or production, to entertain and inform people. | Poor | N/A | Creative industries | |
| 359 | Baker | Kaitunu ParΔoa | Bakers prepare, bake and decorate bread, rolls, pastries, desserts, cakes and slices. | Good | 2-3 years | Services industries, Manufacturing and technology | 30 per hour |
| 360 | Journalist | KairΔ«poata Pepa | Journalists research and produce stories for websites, print, radio, television and other media. | Poor | 1-3 years | Services industries, Creative industries | 100K per year |
| 361 | Communications Professional | Ngaio Whakawhitiwhiti KΕrero | Communications professionals develop strategies to promote the image of organisations to the public, shareholders and employees. | Good | 1-3 years | Services industries, Creative industries | 130K per year, 180K per year |
| 362 | Mechanical Engineering Technician | Kaihangarau Take PΕ«kaha | Mechanical engineering technicians help mechanical engineers design, develop, test and manufacture mechanical devices, including tools, engines and machines. | Good | 2-4 years | Manufacturing and technology | 65K per year, 140K per year |
| 363 | Market Research Analyst | KaitΔtari Rangahau Hokohoko | Market research analysts collect and analyse data and information, write reports, and make recommendations to their clients based on their research. | Average | 3 years | Services industries | 70K per year, 125K per year |
| 364 | Survey Interviewer | Kaiuiui Rangahautanga | Survey interviewers collect facts and opinions by interviewing people. They conduct interviews for market research companies, government agencies and other organisations. | Average | N/A | Services industries | 40 per hour |
| 365 | Telemarketer | Kaihoko Δ-Waea | Telemarketers promote and sell goods or services by telephone. | Poor | N/A | Services industries | 58K per year |
| 366 | Librarian | Kaitiaki PΔtaka Pukapuka | Librarians identify information that people need, organise it and make sure people can access it. | Good | 3-4 years | Social and community services | 75K per year, 125K per year |
| 367 | Library Assistant | KaiΔwhina Tiaki Pukapuka | Library assistants do a variety of tasks within a library including organising material and helping library users. | Average | N/A | Social and community services | 55K per year, 65K per year |
| 368 | Microbiologist | KaimΔtai Koiora Mororiki | Microbiologists study micro-organisms, such as bacteria, viruses and fungi, and the effects they have on plants, animals and humans. They also develop products from micro-organisms to benefit humans or the environment. | Good | 3-9 years | Primary industries | 75K per year, 130K per year |
| 369 | Motor Vehicle Salesperson | Kaihoko Waka Huarahi | Motor vehicle salespeople sell new or used cars, trucks, and other vehicles. | Good | N/A | Services industries | 50K per year, 220K per year |
| 370 | Meat/βSeafood Process Worker | Kaimahi MΔ«ti/βMΔtaitai | Meat/seafood process workers process, grade and package meat, fish or shellfish for local and overseas markets. Some also slaughter animals. | Good | N/A | Primary industries, Manufacturing and technology | 86K per year |
| 371 | Legal Executive | Δpiha Mahi Ture | Legal executives help lawyers prepare and file legal documents, research and prepare cases, give legal advice and help with house sales. | Average | 2 years | Services industries | 70K per year |
| 372 | Personal Assistant | KaiΔwhina Whaiaro | Personal assistants provide administrative, clerical, secretarial and general support to managers and other professionals. They may also be responsible for financial planning, recruitment and staffing. | Poor | N/A | Services industries | 1100K per year |
| 373 | Meteorologist | Matapae Huarere | Meteorologists study the atmosphere to understand and predict weather and climate. | Average | 4-6 years | Services industries | 60K per year, 165K per year |
| 374 | Line Mechanic | Kaimahi Waea Kawe KΕrero | Line mechanics install, repair and maintain overhead and underground power lines. | Good | 2 years | Construction and infrastructure, Manufacturing and technology | 60K per year, 100K per year |
| 375 | Civil Engineer | Mataaro Metarahi | Civil engineers plan, organise and oversee the building and maintenance of structures such as dams, bridges, sewerage systems and roads. | Good | 4 years | Construction and infrastructure, Manufacturing and technology | 110K per year |
| 376 | Quantity Surveyor | KairΕ«ri Utu Hanga Whare | Quantity surveyors manage finances for constructions, calculate budgets and prepare detailed estimates to ensure budgets are sufficient. | Good | 2-3 years | Construction and infrastructure | 100K per year, 170K per year |
| 377 | Nanny/βChild Carer | Kaitiaki Tamariki | Nannies/child carers are responsible for the care, wellbeing and education of infants, toddlers and children in the home. | Average | N/A | Social and community services | 30 per hour |
| 378 | Outdoor Recreation Guide/βInstructor | KaiΔrahi o Waho/βKaiwhakaako o Waho | Outdoor recreation guides and instructors teach or guide outdoor activities such as rafting, kayaking, canyoning, skiing, hunting, climbing, caving and mountain biking. | Poor | N/A | Services industries | 58K per year |
| 379 | Printer | Kaiwhakamahi MΔ«hini TΔ | Printers set up and operate printing machines that print text and images on paper, card, carton, plastics or metal. | Good | 2-3 years | Manufacturing and technology | 50K per year, 93K per year |
| 380 | Print Finisher | Kaiwhakatau TΔnga | Print finishers bind, finish and repair books and other publications by hand or by machine. | Good | 3 years | Manufacturing and technology | 65K per year |
| 381 | Accounts Officer | Kaituhi Kaute | Accounts officers are responsible for monitoring and managing financial accounts for their organisation. | Good | N/A | Services industries | 82K per year |
| 382 | Payroll Officer | Δpiha Utu Kaimahi | Payroll officers arrange payment of staff salaries and wages. | Good | N/A | Services industries | 60K per year, 130K per year |
| 383 | Osteopath | Kaiwhakamaimoa KΕiwi | Osteopaths diagnose muscular and skeletal injuries and treat them using manual techniques such as stretching, massage and manipulation. | Good | 4-5 years | Social and community services | 60K per year, 100K per year |
| 384 | Product Assembler | Kaiwaihanga Taputapu | Product assemblers put together parts of metal products, machinery, electrical and electronic equipment, telecommunications equipment, joinery products and other goods. | Poor | N/A | Manufacturing and technology | 50K per year |
| 385 | Radio Presenter | MΔngai Reo Irirangi | Radio presenters prepare and present news, music, interviews and other radio programmes to entertain and inform audiences. | Poor | N/A | Creative industries | 70K per year, 90K per year |
| 386 | Pulp and Paper Mill Operator | Kaiwhakamahi MΔ«ra Puru RΔkau | Pulp and paper mill operators operate, maintain and repair machines that make pulp and paper. | Poor | 1-2 years | Manufacturing and technology, Primary industries | 50K per year, 100K per year |
| 387 | Human Resources Adviser | Kaitohutohu PΕ«manawa Tangata | Human resources advisers are responsible for recruitment advice, performance management and pay, wellbeing, training and development, employment relations and policies for the staff of an organisation. | Average | 1-3 years | Services industries | 115K per year, 200K per year |
| 388 | Recruitment Consultant | Kaitohutohu Whiwhi Kaimahi | Recruitment consultants help match people with jobs. They work with candidates (people looking for work) and clients (employers looking for staff). | Average | N/A | Services industries | 90K per year, 130K per year |
| 389 | Retail Manager | Kaiwhakahaere Hokohoko | Retail managers organise and manage the operations of retail stores or departments, including staff. | Average | N/A | Services industries | 48K-$60K per year |
| 390 | Retail Sales Assistant | KaiΔwhina Hokohoko | Retail sales assistants serve customers, look after stock items and process sales. | Average | N/A | Services industries | $48K per year |
| 391 | Ship's Officer | Δpiha Kaipuke | Ship's officers navigate and control the safe operation of a ship and supervise and co-ordinate the activities of deck crew. | Good | 2 years | Services industries | 130K per year |
| 392 | Ship's Master | Poutikanga Whakahaere Kaipuke | Ship's masters are in charge of a ship, its crew and any passengers or cargo it is carrying β on the water and in port. On tugs or pilot boats, ship's masters may guide or assist ships in and out of harbours or through difficult waterways. | Good | 2-3 years | Services industries | 70K per year, 250K per year |
| 393 | Sports Coach/βOfficial | Pouako/βKΔtipa HΔkinakina | Sports coaches/officials coach and instruct athletes, and are the officials in charge of sporting events. | Poor | >1 year | Services industries | 80K per year |
| 394 | Sound Technician | Kaihangarau Oro | Sound technicians operate and maintain the equipment used to record, mix and amplify sound for radio, film, television, music and events. | Poor | N/A | Creative industries | 80K per year |
| 395 | Recreation Co-ordinator | Kaiwhakarite HΔkinakina | Recreation co-ordinators plan and run community leisure, before and after-school and sport programmes at venues such as recreation centres, parks, clubs and schools. | Good | N/A | Services industries, Social and community services | 57K per year, 83K per year |
| 396 | Shearer | Kaikuti Hipi | Shearers cut the wool from sheep with clippers. | Average | N/A | Primary industries | 65K per year, 130K per year |
| 397 | Signmaker | Kaihanga Tohu | Signmakers design, print and install signs in a range of materials, for indoor and outdoor use. | Average | 4 years | Services industries, Creative industries | 70K per year |
| 398 | Saw Doctor | Kaiwhakatika Kani | Saw doctors set and adjust sawmill saws and repair, grind and sharpen hand, band and circular saws. | Good | 2-3 years | Manufacturing and technology, Primary industries | 65K per year |
| 399 | Demonstrator | Kaiwhakaatu | Demonstrators show and explain goods and services to potential customers, and promote new lines of products and services. | Good | N/A | Services industries | 28 per hour |
| 400 | Visual Merchandiser | KaiwhakatΕ« Taonga Δ-Matapihi | Visual merchandisers arrange goods and make displays in shops, shop windows and at events to attract the attention of customers. | Average | N/A | Services industries, Creative industries | 55K per year, 200K per year |
| 401 | Water/βWastewater Treatment Operator | Kaiwhakamahi Whakapai Wai (Paru) | Water/wastewater treatment operators run water and wastewater treatment plants. They treat water so that it is acceptable to drink. They also control the disposal of sewage and industrial wastewater. | Good | 2 years | Construction and infrastructure, Manufacturing and technology | 70K per year, 130K per year |
| 402 | Winemaker | Kaihanga Waina | Winemakers make wine from grapes and other fruit. | Average | N/A | Manufacturing and technology, Primary industries | 83K per year, 211K per year |
| 403 | Stevedore | Kaimahi Taunga Waka | Stevedores operate a variety of heavy machinery to load, unload, tally and stow the cargo of a ship. | Average | 1 year | Services industries | 80K per year, 130K per year |
| 404 | Viticulturist | Kaiparuauru Kerepe | Viticulturists grow and harvest grapes from grapevines, and manage vineyards. | Good | N/A | Primary industries | 53K per year, 128K per year |
| 405 | Survey Technician | Kaihangarau RΕ«ri Whenua | Survey technicians collect, record, and evaluate geographical information and prepare databases, maps, charts and plans. | Good | 2 years | Construction and infrastructure | 55K per year, 80K per year |
| 406 | Travel Agent/βAdviser | MΔngai Whakarite Haerenga/βKaitohutohu Whakarite Haerenga | Travel agents/advisers provide information about tourism attractions, sell travel, accommodation, tours and attractions, do ticketing, and process payments. | Poor | <1 year | Services industries | 55K per year, 100K per year |
| 407 | Telecommunications Technician | Kaihangarau Torotoro Waea | Telecommunications technicians install, maintain and repair electronic communications equipment in telecommunications networks and internet supply systems. | Good | 1-3 years | Manufacturing and technology | 75K per year, 95K per year |
| 408 | Upholsterer | Kaiwhakapaipai Uhinga Waka | Upholsterers make or repair the springs, padding, linings and covers of furniture. | Average | N/A | Manufacturing and technology, Creative industries | 50K per year, 62K per year |
| 409 | Animal Care Attendant | Kaitiaki Kararehe | Animal care attendants care for, and clean up after animals in places like kennels, pet shops and animal shelters. | Average | N/A | Social and community services, Services industries, Primary industries | 25-$26 per hour |
| 410 | Zookeeper | Kaitiaki Rawhi Whakaaturanga | Zookeepers care for animals in zoos, wildlife parks and aquariums. | Poor | N/A | Services industries | 90K per year, 117K per year |
| 411 | Youth Worker | Kaimahi Taiohi | Youth workers support young people to improve their health, education, training and employment opportunities. | Good | 1-2 years | Social and community services | 60K per year, 74K per year |
| 412 | Wood Processing Worker | Kaimahi Kani RΔkau | Wood processing workers set up and use woodworking machinery to cut logs into timber for building, furniture and other products. | Average | 1-2 years | Manufacturing and technology, Primary industries | 50K per year |
| 413 | Biosecurity Officer | Δpiha Ao Koiora | Biosecurity officers check areas of land for harmful animals or plants and arrange for, or help with, pest destruction and control. | Good | 3 years | Social and community services, Primary industries | 80K per year, $100K per year |
| 414 | Hunter/βTrapper | Kaikimi/βKaihopu Kararehe | Hunters and trappers shoot or trap animals to remove pests and control disease, and for food, fur, or research. | Average | N/A | Primary industries | |
| 415 | Naturopath | Kaihaumanu Hauora Aro Tini | Naturopaths diagnose and treat health problems with nutritional and lifestyle advice, herbal medicines and natural therapies. | Poor | 2-3 years | Social and community services | 120K per year |
| 416 | Dog Trainer | Kaiako KurΔ« | Dog trainers train dogs and diagnose and treat their behaviour problems. | Poor | N/A | Primary industries, Services industries | 50K per year |
grouped = df_jobprofiles.groupby(['Job Opportunities','Industry'])
grouped.groups
{('Average', 'Construction and infrastructure'): [130, 175, 176, 200], ('Average', 'Construction and infrastructure, Creative industries'): [187], ('Average', 'Construction and infrastructure, Manufacturing and technology'): [136, 174, 258], ('Average', 'Construction and infrastructure, Manufacturing and technology, Creative industries'): [282, 283], ('Average', 'Construction and infrastructure, Primary industries'): [254], ('Average', 'Construction and infrastructure, Primary industries, Creative industries'): [182], ('Average', 'Construction and infrastructure, Services industries'): [0, 327], ('Average', 'Construction and infrastructure, Services industries, Manufacturing and technology'): [54], ('Average', 'Creative industries'): [51, 297, 330], ('Average', 'Manufacturing and technology'): [16, 48, 213, 215, 357], ('Average', 'Manufacturing and technology, Construction and infrastructure'): [251], ('Average', 'Manufacturing and technology, Creative industries'): [49, 346, 347, 348, 408], ('Average', 'Manufacturing and technology, Primary industries'): [312, 402, 412], ('Average', 'Manufacturing and technology, Services industries'): [77], ('Average', 'Manufacturing and technology, Services industries, Creative industries'): [244, 354], ('Average', 'Primary industries'): [19, 88, 109, 110, 139, 305, 338, 396, 414], ('Average', 'Primary industries, Services industries'): [21, 307, 355], ('Average', 'Primary industries, Social and community services'): [157], ('Average', 'Services industries'): [8, 22, 44, 53, 81, 93, 153, 180, 204, 207, 209, 229, 239, 252, 253, 284, 285, 314, 316, 319, 328, 332, 333, 334, 353, 363, 364, 371, 373, 387, 388, 389, 390, 403], ('Average', 'Services industries, Creative industries'): [80, 238, 294, 299, 308, 350, 397, 400], ('Average', 'Services industries, Manufacturing and technology'): [64, 318], ('Average', 'Services industries, Primary industries'): [181], ('Average', 'Services industries, Social and community services'): [105, 106, 108, 205, 303], ('Average', 'Social and community services'): [34, 103, 104, 113, 135, 140, 141, 142, 144, 149, 161, 196, 234, 237, 265, 280, 291, 367, 377], ('Average', 'Social and community services, Primary industries'): [191, 250], ('Average', 'Social and community services, Services industries'): [73], ('Average', 'Social and community services, Services industries, Primary industries'): [409], ('Good', 'Construction and infrastructure'): [2, 32, 36, 37, 45, 46, 50, 55, 62, 65, 134, 162, 163, 165, 202, 208, 220, 230, 231, 376, 405], ('Good', 'Construction and infrastructure, Creative industries'): [129, 255], ('Good', 'Construction and infrastructure, Manufacturing and technology'): [59, 128, 262, 289, 374, 375, 401], ('Good', 'Construction and infrastructure, Primary industries'): [232], ('Good', 'Construction and infrastructure, Primary industries, Manufacturing and technology'): [78], ('Good', 'Construction and infrastructure, Services industries'): [190], ('Good', 'Construction and infrastructure, Services industries, Manufacturing and technology'): [124], ('Good', 'Construction and infrastructure, Social and community services'): [186], ('Good', 'Manufacturing and technology'): [4, 23, 28, 47, 60, 66, 75, 76, 91, 95, 117, 119, 123, 127, 132, 133, 160, 272, 273, 275, 276, 277, 278, 279, 287, 288, 290, 302, 342, 345, 362, 379, 380, 407], ('Good', 'Manufacturing and technology, Construction and infrastructure'): [164, 222, 320], ('Good', 'Manufacturing and technology, Creative industries'): [349], ('Good', 'Manufacturing and technology, Primary industries'): [5, 96, 216, 313, 398], ('Good', 'Manufacturing and technology, Services industries'): [26, 94, 126, 211, 263, 321, 341], ('Good', 'Manufacturing and technology, Social and community services'): [292], ('Good', 'Primary industries'): [9, 13, 17, 20, 61, 111, 112, 152, 171, 172, 177, 178, 206, 214, 322, 323, 324, 337, 344, 368, 404], ('Good', 'Primary industries, Construction and infrastructure'): [52], ('Good', 'Primary industries, Manufacturing and technology'): [370], ('Good', 'Primary industries, Social and community services'): [31], ('Good', 'Services industries'): [6, 7, 24, 35, 38, 40, 42, 58, 63, 69, 70, 71, 74, 82, 89, 90, 97, 99, 100, 118, 166, 168, 169, 170, 179, 183, 194, 199, 217, 218, 223, 249, 256, 267, 300, 301, 309, 310, 311, 315, 329, 336, 356, 369, 381, 382, 391, 392, 399], ('Good', 'Services industries, Construction and infrastructure'): [351], ('Good', 'Services industries, Creative industries'): [79, 138, 335, 361], ('Good', 'Services industries, Manufacturing and technology'): [359], ('Good', 'Services industries, Social and community services'): [10, 107, 221, 264, 281, 304, 395], ('Good', 'Services industries, Social and community services, Primary industries'): [120, 167], ('Good', 'Social and community services'): [18, 30, 68, 83, 86, 92, 98, 101, 102, 116, 121, 122, 125, 131, 137, 143, 145, 146, 148, 154, 155, 156, 158, 159, 185, 192, 195, 201, 203, 224, 226, 227, 235, 240, 245, 247, 259, 261, 270, 271, 274, 306, 317, 325, 326, 331, 366, 383, 411], ('Good', 'Social and community services, Manufacturing and technology'): [84, 233], ('Good', 'Social and community services, Primary industries'): [413], ('Good', 'Social and community services, Services industries'): [147, 150], ('Poor', 'Construction and infrastructure'): [173], ('Poor', 'Construction and infrastructure, Manufacturing and technology'): [56], ('Poor', 'Creative industries'): [1, 11, 12, 14, 188, 197, 198, 210, 219, 241, 243, 260, 295, 298, 358, 385, 394], ('Poor', 'Manufacturing and technology'): [39, 212, 257, 384], ('Poor', 'Manufacturing and technology, Creative industries'): [286, 296, 340], ('Poor', 'Manufacturing and technology, Primary industries'): [386], ('Poor', 'Manufacturing and technology, Services industries'): [33], ('Poor', 'Manufacturing and technology, Social and community services'): [184], ('Poor', 'Primary industries, Services industries'): [416], ('Poor', 'Primary industries, Social and community services'): [151], ('Poor', 'Services industries'): [3, 25, 43, 85, 87, 228, 236, 246, 293, 339, 365, 372, 378, 393, 406, 410], ('Poor', 'Services industries, Construction and infrastructure'): [189], ('Poor', 'Services industries, Creative industries'): [41, 57, 193, 242, 343, 352, 360], ('Poor', 'Services industries, Social and community services'): [114, 268], ('Poor', 'Social and community services'): [15, 27, 67, 72, 225, 248, 266, 269, 415], ('Poor', 'Social and community services, Services industries, Creative industries'): [29, 115]}
df_jobprofiles1=df_jobprofiles.iloc[grouped.groups['Good', 'Manufacturing and technology']]
df_jobprofiles1
| Occupation | Sub Title | Description | Job Opportunities | Training Required | Industry | Earnings | |
|---|---|---|---|---|---|---|---|
| 4 | Electronics Engineer | Mataaro TΔhiko | Electronics engineers design and oversee production of electronic equipment such as radios, televisions, computers, washing machines and telecommunication systems. They may also work in sales and technical support. | Good | 4 years | Manufacturing and technology | $100K per year |
| 23 | Telecommunications Engineer | Mataaro Whitiwhiti KΕrero | Telecommunications engineers design, test and build telecommunications networks and systems. | Good | 2-4 years | Manufacturing and technology | 65K per year, 140K per year |
| 28 | Chemical Engineer | Mataaro MatΕ« | Chemical engineers design, develop and operate equipment and processes used to manufacture chemicals and products. | Good | 3-4 years | Manufacturing and technology | $100K per year |
| 47 | Plastics Technician | Kaihangarau Kirihou | Plastics technicians set up, adjust, maintain and repair machines that manufacture plastic products. | Good | 3 years | Manufacturing and technology | 25 per hour, 34 per hour |
| 60 | Glass Processor | Kaiwhakarite Karaehe | Glass processors prepare and process sheets of flat glass into products such as windows and mirrors for installation in buildings and related structures. | Good | 3 years | Manufacturing and technology | 25 per hour, 36 per hour |
| 66 | Recycler/βDismantler | Kaihangarua/βKaiwetewete | Recyclers/dismantlers take apart, separate, sort and sell materials to be recycled or reused. | Good | N/A | Manufacturing and technology | 25 per hour |
| 75 | Automotive Electrician | Kaimahi Hiko Δ-Waka | Automotive electricians install, maintain and repair electrical wiring, parts and electrical and electronic systems in vehicles. | Good | 3-4 years | Manufacturing and technology | 45 per hour |
| 76 | Coachbuilder/βTrimmer | Kaihanga Pahi/βKaiwhakarΔkei Waka | Coachbuilders manufacture and assemble frames, panels and parts for vehicles such as buses and motor homes. Vehicle trimmers install and repair the upholstery of vehicles. | Good | 3 years | Manufacturing and technology | 23-$26 per hour |
| 91 | Electrical Engineer | Mataaro PΕ«hiko | Electrical engineers design, construct and manufacture electrical systems. They also maintain, operate and manage these systems. | Good | 4 years | Manufacturing and technology | 160K per year, 210K per year |
| 95 | Network Administrator | Kaiwhakahaere Whatunga | Network administrators design, install and maintain computer hardware and software networks, from one-building LANs (local area networks) to worldwide WANs (wide area networks). | Good | 1-3 years | Manufacturing and technology | 140K per year |
| 117 | Data Analyst | KaitΔtari Raraunga | Data analysts identify and describe data trends using statistics and specialised software to help organisations achieve their business aims. | Good | 2-3 years | Manufacturing and technology | 120K per year, 170K per year |
| 119 | User Experience Designer | Kaihoahoa Wheako Whakamahi | User experience (UX) designers design how products such as websites and apps look and work, based on users' needs. | Good | 1-3 years | Manufacturing and technology | 175K per year |
| 123 | Information Technology Architect | Kaihoahoa Hangarau PΔrongo | Information technology (IT) architects analyse an organisation's IT needs, recommend solutions and oversee their delivery and implementation. | Good | 3-5 years | Manufacturing and technology | 200K per year |
| 127 | Systems Administrator | Kaiwhakahaere PΕ«naha | Systems administrators develop, maintain and administer computer operating systems, database management systems, and security policies and procedures. | Good | 2-4 years | Manufacturing and technology | 120K per year, 145K per year |
| 132 | Automotive Technician | Kaihangarau PΕ«kaha Waka | Automotive technicians service and repair vehicles and their parts and systems. | Good | 3-4 years | Manufacturing and technology | 40 per hour, 45 per hour |
| 133 | Automotive Refinisher | Kaipeita Waka | Automotive refinishers prepare vehicle surfaces, match and mix colours, and apply paint to vehicles. | Good | 3-4 years | Manufacturing and technology | 28 per hour, 38 per hour |
| 160 | Mechanical Engineer | Mataaro PΕ«kaha | Mechanical engineers design and give advice on the building and repair of machines and tools. They also investigate problems and faults with machinery, and study ways to improve manufacturing and energy production. | Good | 3-4 years | Manufacturing and technology | 105K per year, 180K per year |
| 272 | Business Analyst | KaitΔtari Pakihi | Business analysts design or recommend solutions, such as computers or computer programs, to help organisations meet their goals. | Good | 2-3 years | Manufacturing and technology | 140K per year, 160K per year |
| 273 | Information Technology Helpdesk/βSupport Technician | Kaihangarau Δwhina Hangarau PΔrongo | Information technology (IT) helpdesk/support technicians set up computer and IT equipment and identify and fix hardware and software problems | Good | 1-3 years | Manufacturing and technology | 90K per year, 120K per year |
| 275 | Security Analyst | KaitΔtari Whakamarumaru | Security analysts create and monitor security processes and frameworks to protect an organisations information systems and computer networks from being illegally accessed. | Good | 1-4 years | Manufacturing and technology | 200K per year, 500K per year |
| 276 | Security Consultant | Kaitohutohu Whakamarumaru | Security consultants identify security weakness in information technology (IT), advise on IT security, and design IT security systems. | Good | 2-3 years | Manufacturing and technology | 200K per year, 500K per year |
| 277 | Test Analyst | KaitΔtari WhakamΔtautau | Test analysts design and carry out tests for computer software and systems, analyse results, and identify and report problems. | Good | 1-3 years | Manufacturing and technology | 130K per year, 200K per year |
| 278 | Scrum Master | Kaitakawaenga Kakari | Scrum masters use the scrum software development approach to keep IT project teams on track. They also help remove obstacles to progress. | Good | 2-3 years | Manufacturing and technology | 200K per year |
| 279 | Penetration Tester | KaiwhakamΔtautau MΕ«rere | Penetration testers investigate security weaknesses in online systems and databases. | Good | 1-4 years | Manufacturing and technology | 200K per year |
| 287 | Software Developer | Kaihanga PΕ«manawa Rorohiko | Software developers create and maintain computer software, websites and software applications (apps). | Good | 1-3 years | Manufacturing and technology | 160K per year |
| 288 | Electrical Engineering Technician | Kaihangarau Take PΕ«hiko | Electrical engineering technicians interpret the designs and technical instructions of electrical engineers, then ensure they are accurately carried out. They also develop, install, test and maintain equipment that produces, transmits or uses power. | Good | 2-3 years | Manufacturing and technology | 27 per hour, 43 per hour |
| 290 | Refrigeration/βAir-conditioning Technician | Kaihangarau PΕ«rere WhakamΔtao | Refrigeration/air-conditioning technicians install, service and maintain refrigeration and air-conditioning systems. | Good | 0-4 years | Manufacturing and technology | 95K per year |
| 302 | Aircraft Maintenance Engineer | Mataaro Whakatika Waka Rererangi | Aircraft maintenance engineers keep aeroplanes safe. They install, inspect, maintain and repair aircraft, aircraft radio, avionic (electronic), navigation, communication and electrical and mechanical systems. | Good | 3-5 years | Manufacturing and technology | 91K per year, 165K per year |
| 342 | Engineering Machinist | Kaiwhakamahi PΕ«rere | Engineering machinists create, assemble and repair metal products by interpreting designs, measuring metals, and operating machines to cut and shape them. | Good | 4 years | Manufacturing and technology | 48K-$103K per year |
| 345 | Electronics Trades Worker | Ringarehe TΔhiko | Electronics trades workers assemble, install and fix electronic parts and equipment. | Good | 3-4 years | Manufacturing and technology | 80K per year, 120K per year |
| 362 | Mechanical Engineering Technician | Kaihangarau Take PΕ«kaha | Mechanical engineering technicians help mechanical engineers design, develop, test and manufacture mechanical devices, including tools, engines and machines. | Good | 2-4 years | Manufacturing and technology | 65K per year, 140K per year |
| 379 | Printer | Kaiwhakamahi MΔ«hini TΔ | Printers set up and operate printing machines that print text and images on paper, card, carton, plastics or metal. | Good | 2-3 years | Manufacturing and technology | 50K per year, 93K per year |
| 380 | Print Finisher | Kaiwhakatau TΔnga | Print finishers bind, finish and repair books and other publications by hand or by machine. | Good | 3 years | Manufacturing and technology | 65K per year |
| 407 | Telecommunications Technician | Kaihangarau Torotoro Waea | Telecommunications technicians install, maintain and repair electronic communications equipment in telecommunications networks and internet supply systems. | Good | 1-3 years | Manufacturing and technology | 75K per year, 95K per year |
3.7.2 Transfer the earnings column from text to number.ΒΆ
df = pd.DataFrame(df_jobprofiles1)
def split_earnings(earnings):
earnings = earnings.replace("K", "000").replace("per hour", "").replace("per year", "").replace("$", "").strip()
earnings = earnings.replace("β", "-")
try:
if "," in earnings:
parts = earnings.split(",")
min_earning = float(parts[0].split("-")[0].strip())
max_earning = float(parts[1].split("-")[1].strip())
elif "-" in earnings:
parts = earnings.split("-")
min_earning = float(parts[0].strip())
max_earning = float(parts[1].strip())
else:
min_earning = max_earning = float(earnings.strip())
except Exception as e:
print(f"Error processing earnings '{earnings}': {e}")
min_earning = max_earning = None
return min_earning, max_earning
df[['Min Earnings', 'Max Earnings']] = df['Earnings'].apply(split_earnings).apply(pd.Series)
df
Error processing earnings '115000β140000 , 145000β160000': could not convert string to float: '115000β140000'
| Occupation | Sub Title | Description | Job Opportunities | Training Required | Industry | Earnings | Min Earnings | Max Earnings | |
|---|---|---|---|---|---|---|---|---|---|
| 4 | Electronics Engineer | Mataaro TΔhiko | Electronics engineers design and oversee production of electronic equipment such as radios, televisions, computers, washing machines and telecommunication systems. They may also work in sales and technical support. | Good | 4 years | Manufacturing and technology | $100K per year | 100000.0 | 100000.0 |
| 23 | Telecommunications Engineer | Mataaro Whitiwhiti KΕrero | Telecommunications engineers design, test and build telecommunications networks and systems. | Good | 2-4 years | Manufacturing and technology | 65K per year, 140K per year | 60000.0 | 140000.0 |
| 28 | Chemical Engineer | Mataaro MatΕ« | Chemical engineers design, develop and operate equipment and processes used to manufacture chemicals and products. | Good | 3-4 years | Manufacturing and technology | $100K per year | 100000.0 | 100000.0 |
| 47 | Plastics Technician | Kaihangarau Kirihou | Plastics technicians set up, adjust, maintain and repair machines that manufacture plastic products. | Good | 3 years | Manufacturing and technology | 25 per hour, 34 per hour | 23.0 | 34.0 |
| 60 | Glass Processor | Kaiwhakarite Karaehe | Glass processors prepare and process sheets of flat glass into products such as windows and mirrors for installation in buildings and related structures. | Good | 3 years | Manufacturing and technology | 25 per hour, 36 per hour | 23.0 | 36.0 |
| 66 | Recycler/βDismantler | Kaihangarua/βKaiwetewete | Recyclers/dismantlers take apart, separate, sort and sell materials to be recycled or reused. | Good | N/A | Manufacturing and technology | 25 per hour | 23.0 | 25.0 |
| 75 | Automotive Electrician | Kaimahi Hiko Δ-Waka | Automotive electricians install, maintain and repair electrical wiring, parts and electrical and electronic systems in vehicles. | Good | 3-4 years | Manufacturing and technology | 45 per hour | 32.0 | 45.0 |
| 76 | Coachbuilder/βTrimmer | Kaihanga Pahi/βKaiwhakarΔkei Waka | Coachbuilders manufacture and assemble frames, panels and parts for vehicles such as buses and motor homes. Vehicle trimmers install and repair the upholstery of vehicles. | Good | 3 years | Manufacturing and technology | 23-$26 per hour | 23.0 | 26.0 |
| 91 | Electrical Engineer | Mataaro PΕ«hiko | Electrical engineers design, construct and manufacture electrical systems. They also maintain, operate and manage these systems. | Good | 4 years | Manufacturing and technology | 160K per year, 210K per year | 77000.0 | 210000.0 |
| 95 | Network Administrator | Kaiwhakahaere Whatunga | Network administrators design, install and maintain computer hardware and software networks, from one-building LANs (local area networks) to worldwide WANs (wide area networks). | Good | 1-3 years | Manufacturing and technology | 140K per year | 80000.0 | 140000.0 |
| 117 | Data Analyst | KaitΔtari Raraunga | Data analysts identify and describe data trends using statistics and specialised software to help organisations achieve their business aims. | Good | 2-3 years | Manufacturing and technology | 120K per year, 170K per year | 90000.0 | 170000.0 |
| 119 | User Experience Designer | Kaihoahoa Wheako Whakamahi | User experience (UX) designers design how products such as websites and apps look and work, based on users' needs. | Good | 1-3 years | Manufacturing and technology | 175K per year | 100000.0 | 175000.0 |
| 123 | Information Technology Architect | Kaihoahoa Hangarau PΔrongo | Information technology (IT) architects analyse an organisation's IT needs, recommend solutions and oversee their delivery and implementation. | Good | 3-5 years | Manufacturing and technology | 200K per year | 140000.0 | 200000.0 |
| 127 | Systems Administrator | Kaiwhakahaere PΕ«naha | Systems administrators develop, maintain and administer computer operating systems, database management systems, and security policies and procedures. | Good | 2-4 years | Manufacturing and technology | 120K per year, 145K per year | 85000.0 | 145000.0 |
| 132 | Automotive Technician | Kaihangarau PΕ«kaha Waka | Automotive technicians service and repair vehicles and their parts and systems. | Good | 3-4 years | Manufacturing and technology | 40 per hour, 45 per hour | 25.0 | 45.0 |
| 133 | Automotive Refinisher | Kaipeita Waka | Automotive refinishers prepare vehicle surfaces, match and mix colours, and apply paint to vehicles. | Good | 3-4 years | Manufacturing and technology | 28 per hour, 38 per hour | 23.0 | 38.0 |
| 160 | Mechanical Engineer | Mataaro PΕ«kaha | Mechanical engineers design and give advice on the building and repair of machines and tools. They also investigate problems and faults with machinery, and study ways to improve manufacturing and energy production. | Good | 3-4 years | Manufacturing and technology | 105K per year, 180K per year | 85000.0 | 180000.0 |
| 272 | Business Analyst | KaitΔtari Pakihi | Business analysts design or recommend solutions, such as computers or computer programs, to help organisations meet their goals. | Good | 2-3 years | Manufacturing and technology | 140K per year, 160K per year | NaN | NaN |
| 273 | Information Technology Helpdesk/βSupport Technician | Kaihangarau Δwhina Hangarau PΔrongo | Information technology (IT) helpdesk/support technicians set up computer and IT equipment and identify and fix hardware and software problems | Good | 1-3 years | Manufacturing and technology | 90K per year, 120K per year | 60000.0 | 120000.0 |
| 275 | Security Analyst | KaitΔtari Whakamarumaru | Security analysts create and monitor security processes and frameworks to protect an organisations information systems and computer networks from being illegally accessed. | Good | 1-4 years | Manufacturing and technology | 200K per year, 500K per year | 120000.0 | 500000.0 |
| 276 | Security Consultant | Kaitohutohu Whakamarumaru | Security consultants identify security weakness in information technology (IT), advise on IT security, and design IT security systems. | Good | 2-3 years | Manufacturing and technology | 200K per year, 500K per year | 120000.0 | 500000.0 |
| 277 | Test Analyst | KaitΔtari WhakamΔtautau | Test analysts design and carry out tests for computer software and systems, analyse results, and identify and report problems. | Good | 1-3 years | Manufacturing and technology | 130K per year, 200K per year | 80000.0 | 200000.0 |
| 278 | Scrum Master | Kaitakawaenga Kakari | Scrum masters use the scrum software development approach to keep IT project teams on track. They also help remove obstacles to progress. | Good | 2-3 years | Manufacturing and technology | 200K per year | 120000.0 | 200000.0 |
| 279 | Penetration Tester | KaiwhakamΔtautau MΕ«rere | Penetration testers investigate security weaknesses in online systems and databases. | Good | 1-4 years | Manufacturing and technology | 200K per year | 100000.0 | 200000.0 |
| 287 | Software Developer | Kaihanga PΕ«manawa Rorohiko | Software developers create and maintain computer software, websites and software applications (apps). | Good | 1-3 years | Manufacturing and technology | 160K per year | 110000.0 | 160000.0 |
| 288 | Electrical Engineering Technician | Kaihangarau Take PΕ«hiko | Electrical engineering technicians interpret the designs and technical instructions of electrical engineers, then ensure they are accurately carried out. They also develop, install, test and maintain equipment that produces, transmits or uses power. | Good | 2-3 years | Manufacturing and technology | 27 per hour, 43 per hour | 23.0 | 43.0 |
| 290 | Refrigeration/βAir-conditioning Technician | Kaihangarau PΕ«rere WhakamΔtao | Refrigeration/air-conditioning technicians install, service and maintain refrigeration and air-conditioning systems. | Good | 0-4 years | Manufacturing and technology | 95K per year | 50000.0 | 95000.0 |
| 302 | Aircraft Maintenance Engineer | Mataaro Whakatika Waka Rererangi | Aircraft maintenance engineers keep aeroplanes safe. They install, inspect, maintain and repair aircraft, aircraft radio, avionic (electronic), navigation, communication and electrical and mechanical systems. | Good | 3-5 years | Manufacturing and technology | 91K per year, 165K per year | 48000.0 | 165000.0 |
| 342 | Engineering Machinist | Kaiwhakamahi PΕ«rere | Engineering machinists create, assemble and repair metal products by interpreting designs, measuring metals, and operating machines to cut and shape them. | Good | 4 years | Manufacturing and technology | 48K-$103K per year | 48000.0 | 103000.0 |
| 345 | Electronics Trades Worker | Ringarehe TΔhiko | Electronics trades workers assemble, install and fix electronic parts and equipment. | Good | 3-4 years | Manufacturing and technology | 80K per year, 120K per year | 48000.0 | 120000.0 |
| 362 | Mechanical Engineering Technician | Kaihangarau Take PΕ«kaha | Mechanical engineering technicians help mechanical engineers design, develop, test and manufacture mechanical devices, including tools, engines and machines. | Good | 2-4 years | Manufacturing and technology | 65K per year, 140K per year | 48000.0 | 140000.0 |
| 379 | Printer | Kaiwhakamahi MΔ«hini TΔ | Printers set up and operate printing machines that print text and images on paper, card, carton, plastics or metal. | Good | 2-3 years | Manufacturing and technology | 50K per year, 93K per year | 48000.0 | 93000.0 |
| 380 | Print Finisher | Kaiwhakatau TΔnga | Print finishers bind, finish and repair books and other publications by hand or by machine. | Good | 3 years | Manufacturing and technology | 65K per year | 50000.0 | 65000.0 |
| 407 | Telecommunications Technician | Kaihangarau Torotoro Waea | Telecommunications technicians install, maintain and repair electronic communications equipment in telecommunications networks and internet supply systems. | Good | 1-3 years | Manufacturing and technology | 75K per year, 95K per year | 48000.0 | 95000.0 |
Based on the data from the website, earnings include both hourly and annual salaries. In this study, we will not consider the hourly wage part. After converting the text to numerical values, we categorize the earnings into minimum and maximum values. The next step will involve further analysis.
3.7.3 Visualize the maximum yearly salary by occupation and training years in the manufacturing and technology industries.ΒΆ
df = pd.DataFrame(df_jobprofiles1)
def split_earnings(earnings):
earnings = earnings.replace("K", "000").replace("per hour", "").replace("per year", "").replace("$", "").strip()
earnings = earnings.replace("β", "-")
try:
if "," in earnings:
parts = earnings.split(",")
min_earning = float(parts[0].split("-")[0].strip())
max_earning = float(parts[1].split("-")[1].strip())
elif "-" in earnings:
parts = earnings.split("-")
min_earning = float(parts[0].strip())
max_earning = float(parts[1].strip())
else:
min_earning = max_earning = float(earnings.strip())
except Exception as e:
print(f"Error processing earnings '{earnings}': {e}")
min_earning = max_earning = None
return min_earning, max_earning
df[['Min Earnings', 'Max Earnings']] = df['Earnings'].apply(split_earnings).apply(pd.Series)
df_filtered = df[df['Max Earnings'] >= 5000]
plt.figure(figsize=(10, 6))
plt.barh(df_filtered['Occupation'], df_filtered['Max Earnings'], color='fuchsia')
plt.xlabel('Maximum Earnings')
plt.ylabel('Occupation')
plt.title('Maximum Earnings by Occupation (Yearly Salary) ')
Error processing earnings '115000β140000 , 145000β160000': could not convert string to float: '115000β140000'
Text(0.5, 1.0, 'Maximum Earnings by Occupation (Yearly Salary) ')
The highest yearly salary can be observed for security consultants and security analysts, at around 500,000. πΉππ πππ‘π πππππ¦π π‘π , π‘βπ πππ₯πππ’π π¦πππππ¦ π πππππ¦ ππ ππππππ₯ππππ‘πππ¦ 170,000. According to the website's definition, these highest salaries correspond to more senior or advanced positions.
df = pd.DataFrame(df_jobprofiles1)
def split_earnings(earnings):
earnings = earnings.replace("K", "000").replace("per hour", "").replace("per year", "").replace("$", "").strip()
earnings = earnings.replace("β", "-")
try:
if "," in earnings:
parts = earnings.split(",")
min_earning = float(parts[0].split("-")[0].strip())
max_earning = float(parts[1].split("-")[1].strip())
elif "-" in earnings:
parts = earnings.split("-")
min_earning = float(parts[0].strip())
max_earning = float(parts[1].strip())
else:
min_earning = max_earning = float(earnings.strip())
except Exception as e:
print(f"Error processing earnings '{earnings}': {e}")
min_earning = max_earning = None
return min_earning, max_earning
df[['Min Earnings', 'Max Earnings']] = df['Earnings'].apply(split_earnings).apply(pd.Series)
df_filtered = df[df['Max Earnings'] >= 5000]
df_sorted = df_filtered.sort_values(by='Max Earnings', ascending=False)
plt.figure(figsize=(10, 6))
plt.barh(df_sorted['Training Required'], df_sorted['Max Earnings'], color='indigo')
plt.xlabel('Maximum Earnings')
plt.ylabel('Training Required')
plt.title('Maximum Earnings by Training Required (Yearly Salary) ')
Error processing earnings '115000β140000 , 145000β160000': could not convert string to float: '115000β140000'
Text(0.5, 1.0, 'Maximum Earnings by Training Required (Yearly Salary) ')
From this table, we can see the relationship between maximum earnings and different training years. Since the value of years is defined as open-ended on the website, the results show various combinations. According to the displayed values, training years and maximum wage are not always directly proportional. Instead, maximum earnings are more closely related to the occupation.
SummaryΒΆ
3.1 Discuss the overall GDP changes in New Zealand from 2010 to 2022. What are the top three industrial contributions?
The top three contributing industries are Professional, Scientific, and Technical Services, Manufacturing, and Health Care and Social Assistance.
3.2 Does the industry that contributes the most GDP bring the same proportion of job opportunities?
Industries contributing the most to GDP do not provide the most jobs.
3.3 What are the job opportunities in the six main industries based on the occupation listed on Careers.NZ?
Manufacturing and Technology sectors have the best prospects, Construction and Infrastructure also fare well, while the Creative Industries face poor prospects.
3.4 As the Largest City in New Zealand and a Major Economic Hub, is Auckland the Best in Terms of All Vacancy Indices (AVIs) and GDP from 2010 to 2022?
Auckland has the highest GDP contribution but not the highest AVIs, with regions like Bay of Plenty and Otago Southland often showing higher AVI values.
3.5 How Does Unemployment Rate in New Zealand Relate to Auckland's AVI and GDP?
High unemployment correlates with lower AVIs and vice versa. Auckland's GDP improvement correlates with lower unemployment rates over time.
3.6 Does the Job Creation in Auckland from 1999 to 2022 Remain Positive, Making Auckland the City with the Most Economic Contribution?
From 1999 to 2022, Auckland's job market showed a generally positive trend, with job creation consistently outpacing job destruction. However, there were a couple of exceptions, notably in 1999 and 2019, where more jobs were lost than created. These dips could be attributed to specific economic policies or significant events like the COVID-19 pandemic in 2019.
3.7 What are the Employment Prospects and Highest Salary Ranges for the Manufacturing and Technology Industries, as well as for Data Analyst Positions?
The highest yearly salaries in the manufacturing and technology industries, particularly for senior roles such as security consultants and security analysts, can reach up to 500,000. For data analysts, the maximum yearly salary is approximately 170,000. According to the data, maximum earnings are more closely related to the specific occupation rather than the number of training years, indicating that higher salaries are often associated with more senior or advanced positions within these industries.
ConclusionΒΆ
The use of API retrieval and web scraping has elevated the scope of our analysis beyond static documents. This process allows us to incorporate information from multiple perspectives, providing a comprehensive analysis of New Zealand's GDP changes, industry contributions, job opportunities, and salary ranges from 2010 to 2022. It is evident that New Zealand's GDP has grown steadily over the years, with minimal fluctuations between industries.
Despite the uncertainties in global markets and changes in domestic policies that have increased volatility in the employment market, the technology and manufacturing sectors have shown significant growth potential, particularly for senior positions. Auckland, as an economic hub, leads in GDP contribution but has room for improvement in vacancy indices compared to other cities. The technology industry is a major pillar of New Zealand's economy, and our field of study(Master of Analytics) shows optimistic and promising trends in the overall market. Overall, understanding these dynamics is key to taking advantage of opportunities and addressing challenges in New Zealand's ever-changing job market.